A Fuzzy Choquet Integral with an Interval Type-2 Fuzzy Number-Valued Integrand
|
|
- Ethan Floyd
- 5 years ago
- Views:
Transcription
1 A Fuzzy Choquet Integral with an Interval Type-2 Fuzzy Number-Valued Integrand Timothy C. Havens, Student Member, IEEE,, Derek T. Anderson, Student Member, IEEE, and James M. Keller, Fellow, IEEE Abstract Fuzzy integrals have been used to fuse the evidence or opinions from a variety of sources. These integrals are nonlinear combinations of the support functions and the possibly subective worth of subsets of the sources of information, realized by a fuzzy measure. There have been many applications and extensions of fuzzy integrals and this paper proposes a fuzzy Choquet integral, where the integrand takes an interval type-2 fuzzy number and the fuzzy measure is real numbervalued. Interval type-2 fuzzy numbers encode the second-order uncertainty in a fuzzy number. Type-2 fuzzy numbers have been been shown to be useful in many applications, including computing with words and control systems. We illustrate our method on several numerical examples as well as on a bioinformatics application. I. INTRODUCTION Let X = {x,x 2,...,x N } be a non-empty finite set typically of information sources or evidence [] and g : 2 X [, ] be a fuzzy measure with the following properties [2]: gø =, gx =; 2 If A B X then ga gb g is nondecreasing. The measure g is the possibly subective confidence or worth of each subset of information sources; hence property tells us that the worth of no sources, the empty set Ø, is and the worth of all sources, the universal set X, is. Property 2 follows intuition, in that two sources are worth at least as much as one, three sources are worth at least as much as two, and so on. A well-known fuzzy measure is the Sugeno λ-measure [2], which for sets A X and B X, such that A B =Ø, g λ A B =g λ A+g λ B+λg λ Ag λ B. It is proven that for a given set of densities g i, where g i = g{x i }, λ can be determined by solving λ += n +λg i,λ>. 2 i= The λ-measure is especially attractive because one only has to provide the measures of the singletons; the densities of the non-singleton sets are calculated using. Another T.C. Havens, D.T. Anderson, and J.M. Keller are with the Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO 652, USA phone: , havenst@gmail.com, keller@missouri.edu, dtaxtd@missouri.edu. fuzzy measure that is built using the densities g i is the decomposable measure [3] ga =min, g i,a X. 3 i,x i A This measure has the added benefit that it is easy and fast to compute. There are many forms of the fuzzy integral []. In practice, fuzzy integrals are used for evidence fusion [4 6]. They combine sources of information by accounting for both the support of the question the evidence and the expected worth of each subset of sources as supplied by a fuzzy measure g. In this paper, we focus on the discrete fuzzy Choquet integral [7] proposed by Murofushi and Sugeno [8]. Let h : X [, be a real-valued function which represents the evidence or support of a particular hypothesis generally when dealing with sensor fusion problems, h : X [, ]. The discrete fuzzy Choquet integral is defined as C g h = N hx πi ga πi ga πi+, 4 i= where π is a permutation of X, such that hx π hx π2... hx πn, A πi = {x πi,...,x πn }, and ga πn+ =. In some cases, the evidence h cannot or should not be represented simply by numbers; h would be better represented as an interval-valued or fuzzy number-valued function. An example is the survey question, How many bottles of wine should I purchase for the reception?. Many people would answer this question with an interval, e.g. between 2 and 3. Thus an interval-valued h is more appropriate. In other situations fuzzy number-valued integrands would better suit the problem, e.g. around 25. Extensions of both the fuzzy Sugeno and fuzzy Choquet integral for both interval-valued and fuzzy number-valued integrands have been proposed in [9 ]. Let H =[h,h + ] and H be the interval-valued and fuzzy number-valued functions, respectively. The Choquet integrals of H and H are defined as C g H [ C g h,c g h + ] 5 C g Ha sup {α [, ]; a C g α H}, 6 where α H =[ α h, α h + ] is the closed interval of the levelcut of H at α [, ]. Notice that 6 is related to 5 by
2 TABLE I NOTATION X set of sources sensors, inputs, etc. x i ith source hx i evidence offered by x i h real-valued function H =[h,h + ] interval-valued function H type- fuzzy number-valued function H interval type-2 fuzzy number-valued function α H =[ α h, α h + ] level-cut of H at α g i numerical worth of x i g λ Sugeno fuzzy λ-measure [2] the representation theorem and extension principle [, 2], C g H = αc g α H. 7 α The output of a fuzzy Choquet integral with an intervalvalued integrand H is an interval and the output with a fuzzy number-valued integrand H is a fuzzy number. In this paper we extend 5 and 6 to interval type-2 fuzzy number-valued integrands. Type-2 fuzzy sets encode higherorder uncertainty by essentially blurring the membership function of a type- fuzzy set, thus allowing the membership function to be uncertain itself. Type-2 fuzzy sets have been found to be especially useful in the Computing with Words CW paradigm, proposed by Zadeh in [3], and in the most notable CW implementation, Mendel s perceptual computer [4 6]. Section II provides a detailed description and the necessary theoretical background of interval type-2 fuzzy sets. In Section III we develop the fuzzy Choquet integral for interval type-2 fuzzy integrands and in Section IV we offer several illustrative examples, including a bioinformatics application. We summarize in Section V. Please notice Table I which provides a list of the notation used in this paper. II. INTERVAL TYPE-2 FUZZY SETS Type-2 fuzzy sets T2 FSs and fuzzy numbers FNs [7] provide an additional level of uncertainty over Type- fuzzy sets T FSs, in that the T membership function MF has an underlying uncertainty in its values. Figure illustrates the relationship between a T FS and a T2 FS. The left view shows a T FS H for this section assume that H is equivalent to the fuzzy set Hx. For the T FS, the membership at a is exactly μa ; there is no second-order uncertainty to this value. The right view in Fig. shows how uncertainty can be applied to H to produce a T2 FS, as shown by the gray region around H although, in practice, T2 FSs are not ust blurred T FSs. The gray region is called the footprint of uncertainty FOU and this region defines the uncertainty in the shape of H. Thus for the T2 FS, the value of the membership μa is uncertain. In a generalized T2 FS, the membership μa would have a membership function that describes its T2 uncertainty along the vertical line that intersects with the gray region between μ and μ 2. However in this paper, we focus on interval type-2 fuzzy sets IT2 FSs [8, 9], where the uncertainty of the membership at a given μa μ T FS a H a μ μ 2 μ T2 FS Fig.. Left view shows a type- fuzzy set H. Uncertainty is applied to h in the right view shown by the gray region around H which results in a type-2 fuzzy set. The membership μa in the type-2 fuzzy set has an uncertain value between μ and μ 2. μ a H Embedded T FS UMF H UMF H LMF H Fig. 2. Interval type-2 fuzzy set, h; solid gray region Footprint of Uncertainty, solid line Upper Function, dotted line Lower Function, wavy line embedded type-fuzzy set [9] value a is uniform across a given interval. For example, the uncertainty of μa in the right view of Fig. would have equal membership across the interval [μ,μ 2 ]. We denote an IT2 FS with a tilde, e.g. H. Figure 2 is the quintessential illustration of an IT2 FS [9]. This plot shows that the FOU is bounded by the Upper Function UMF, which lies on the outside of the FOU, and the Lower Function LMF, which lies inside of the FOU. The UMF is the least certain embedded T FS within the IT2 FS; the LMF is the most certain embedded T FS within the IT2 FS. For the sake of completeness and to develop our notation, we now follow much of the theoretical development of IT2 FSs from references [9, 2]. For all a A the membership μa is an interval, thus we denote this membership function as μ a =[μ a,μ + a ]. Wecan write H as the union of μ a, a A H = /a, μ, 8 μ μ a a A where /a, μ indicates a secondary membership of at a, μ. Note that standard notation for union in the literature regarding IT2 FSs is ; however, to eliminate confusion with the Choquet integral notation we choose to use to denote union. Notice that this completely defines the IT2 FS H as the union of the vertical interval-valued sets μ a. The FOU a a
3 of H can be constructed as FOU H = μ a. 9 a A Reference [9] aptly describes this equation as the verticalslice representation of the FOU, as the FOU is constructed from all the vertical intervals μ a. The bounds of the FOU, the UMF and the LMF, are defined as UMF H FOU H, a A, LMF H FOU H, a A. As stated above, the UMF and LMF are fuzzy sets that bound the FOU. These sets are also examples of embedded FSs. The definition of an embedded IT2 FS is H e = [/μ a ]/a, μ a μ a. 2 a A This set has the shape of a T FS with a secondary membership of only at the values μ a μ a for each a. The companion of an embedded IT2 FS is an embedded T FS an example is the wavy line depicted in Fig. 2. An embedded T FS is defined as H e = μ a /a, μ a μ a, 3 a A where μ a is the membership value of the T FS H e at a. Embedded FSs are related by H e =/H e, 4 where this equation implies a T2 membership of along the embedded T FS H e. The embedded FSs in 2 and 3 are important in our definition of the fuzzy Choquet integral for an IT2 fuzzy integrand. The representation theorem from [2] states that H is the union of all its embedded IT2 FSs note that we have extended this to the continuous domain, H = H e, 5 where H e is an embedded IT2 FS in H. In this case, there is an infinite number of embedded IT2 FSs in H, hence the integral notation of the union. Furthermore, the FOU of H can be expressed as the union of the embedded T FSs, FOU H = H e. 6 Note that the union in 6 conforms to the definition of the union in [9]. Finally, H can be expressed as having a T2 membership of in its FOU, H =/FOU H. 7 With the above definitions, we now move on to our definition of the fuzzy Choquet integral. III. EXTENDED FUZZY CHOQUET INTEGRAL We begin our definition of the fuzzy Choquet integral for an IT2 FN-valued integrand by examining the problem. An interval-valued integrand H, as in 5, produces an intervalvalued result, while a FN-valued integrand H, as in 6, produces a FN. It follows that the Choquet integral with an IT2 FN-valued integrand H produces an IT2 FN. We define an IT2 FN as an IT2 FS where the UMF and LMF are type- FNs. Assume an IT2 FN-valued function H, e.g. a mapping of words in a CW system. Define the embedded T FN-valued functions He as the functions Hex i FOU Hxi, i,. Equation 7 shows that an IT2 FS can be constructed from its FOU and 6 shows that the FOU is the union of all its embedded T FSs; these results can also be shown for FNs. Thus, we define the FOU of C g H as the union of all C g He, FOU C g H C g He, 8 and 7 shows that C g H = where C g H is an IT2 FN. Proposition. If Z = Cg H then / FOU C g H, 9 UMF Z = C g UMF H, 2 LMF Z = C g LMF H, 2 where UMF H is the function where Hx i =UMF Hx i, i and LMF H is the function where Hx i =LMF Hx i, i. Proof. The UMF Hx i FSs are the upper-bound of the embedded T FSs in the FOUs of the evidence Hx i ; the LMF Hx i FSs are the lower-bound of the embedded T FSs in the FOUs of Hx i. The left-most points in the level-cuts α UMF Hx i,α [, ] represent the smallest values that the evidence can take for a given α and the right-most points in these level-cuts represent the largest values that the evidence can take. By definition of the FOU, the following property is true for all evidence H, α UMF Hx i α He x i 22 α He + x i α UMF + Hx i, i, where He is any [ embedded T FN-valued function in H and α UMF Hx i = α UMF Hx i, α UMF + Hx ] i. Additionally, the Choquet integral is monotonic; thus, C α g UMF H g He 23 g He + g UMF + H. Note that in the above property H e represents any combination of the embedded T FNs in the FOUs of the IT2 FNs produced by H.
4 Using the same logical steps as above, we can show that for a given set of evidence H, α He x i α LMF Hx i 24 α LMF + Hx i α He + x i, i, and, subsequently, C α g He g LMF H 25 g LMF + H g He +. Equation 6 shows that we can build C g UMF H and C g LMF H from the level-cuts, α UMF H and α LMF H. Thus, 23 and 25 together show that UMF Cg H = C g H = C g UMF H, 26 LMF Cg H = C g H = C g LMF H. 27 Remark. Proposition shows that we can build the UMF and LMF of FOU C g H by performing the Choquet integral on the respective functions on the UMFs and LMFs of the set of evidence H. Now we show that the FOU C g H is completely full. That is, the level-cut α C ghe is composed of the two closed intervals between the UMF and LMF of C g H. Proposition 2. Consider the level-cuts of UMF Cg H and LMF Cg H, where [ ] α UMF Cg H = α UMF C g H,α UMF + C g H, 28 [ ] α LMF Cg H = α LMF C g H,α LMF + C g H 29 and α [, ]. Then, α FOU C g H {[ ] = α UMF C g H,α LMF C g H 3, [ ]} α LMF + C g H,α UMF + C g H, α, α [, ]. The notation {[, ], [, ]} indicates the FOU is composed of two intervals. Proof. We begin by breaking down the extended Choquet integral as in Eqs. 5,7, FOU C g H = C g He = = α α αc g α H e α [ C g α H e,c g α H + e ]. 3 Taking the level-cut of 3 at a value β [, ] produces β α [ C g α He,C g α He + ] = β α α α [ C g α He,C g α He + ] = [ Cg β H e,c g β H + e ]. 32 This equation essentially is the collection of all possible intervals produced by the Choquet integral acting on the level-cuts of the embedded T evidence He. Thus, we prove the proposition by showing that the interval end-points C g β He and C g β He + are interval-valued, themselves. By the definition of the FOU, [ ] α He x i [ α UMF Hx i, α LMF Hx i ], 33 α He + x i α UMF + Hx i, α LMF + Hx i, i. 34 Moreover, because Hx i is an IT2 FN, [ α He x i = α UMF H x i, α LMF i] H x, 35 α H + e x i = [ α UMF + H x i, α LMF + H x i], i. 36 The Choquet integral is monotonic, thus [ ] C g α He = α UMF C g H,α LMF C g H, 37 [ ] C g α He + = α LMF + C g H,α UMF + C g H. 38 These equation show that collection of interval end-points in 32 are interval-valued at the bounds shown in 3, thus proving the proposition. Remark 2. Propositions and 2 show that, in practice, the extended Choquet integral can be calculated by performing the Choquet integral on the UMFs and LMFs of the IT2 FN-valued evidence. A constraint on the methods described here is that the UMFs and LMFs are FNs. In most real applications, the UMFs will be normal FNs have a maximum membership equal to. However, there are many IT2 systems where the LMFs are not normal. Figure 3 shows an example where the LMFs of the evidence are not normal. The methods we describe here will work for these cases as long as the maximum memberships of the LMFs are equal, that is sup LMF Hx =suplmf Hx 2 =...=suplmf Hx N, as is shown in Fig. 3. This is because the FOU of the IT2 FNs shown in Fig. 3 can still be expressed as the union of T FNs.
5 μ Fig. 3. The extended Choquet integral can be used with IT2 FNs that have non-normal convex LMFs if the maximum memberships of the LMFs are equal Hx C g H Hx 3.2 Hx Hx a gλ i = {.5,.5,.5} C g H Hx 3.2 Hx b gλ i = {.3,.5,.8} Fig. 4. Example of fuzzy Choquet integral with an IT2 fuzzy integrand; fuzzy λ-measure. In references [2, 22], the authors propose a method, called the linguistic weighted average, by which the fuzzy weighted average can be applied to IT2 FSs. They devise a scheme where the operation can be applied in the case where the LMFs are of different heights. We are examining their paradigm with the hope of generalizing the extended Choquet integral to this case. IV. NUMERICAL EXAMPLES Example. Figure 4 illustrates an example of our formulation of the fuzzy Choquet integral using the fuzzy λ- measure. The red IT2 fuzzy-numbers, Hx, Hx2, and Hx 3 are the evidence. Figure 4a shows the result C g H for the densities gλ i = {.5,.5,.5}; thus, this example is an average-like operator on H. Figure 4b illustrates C g H for the densities gλ i = {.3,.5,.8}. View b shows that the result reflects the fact that Hx3 has a higher density worth than the other evidence; thus, C g H is more similar to Hx 3. a TABLE II NUMERIC WEIGHTS OF THE RELIABILITY OF GO ANNOTATIONS [23] Evidence code Weight Traceable author statement TAS Inferred from sequence similarity ISS.8 Inferred from electronic annotation IEA.6 Non-traceable author statement NAS.4 Not documented ND. Not recorded NR. A. Gene Ontology similarity measure Reference [23] proposes a Choquet fuzzy integral-based similarity measure for genes and gene products described by Gene Ontology GO [24] annotations. Consider two genes, G = {T,...,T n } and G 2 = {T 2,...,T 2m }, where {T,...,T n } are the n GO annotations of gene G and {T 2,...,T 2m } are m GO annotations of gene G 2. Using these GO-based representations of G and G 2, the similarity of G and G 2 can be computed by considering the set of GO annotation pairs X = G G 2 = {T, T 2,...,T nm } as a finite set of information sources that support the similarity of G and G 2, where T k =T i,t 2 is a pair of terms. Assume that there exists a pairwise term similarity measure st k [, that represents the similarity of the pair of terms T k = T i,t 2 see [25 29] for more discussion on the similarity measure st k. It is easy to see that the evidences h of the similarity of G and G 2 are the nm pairwise similarity values, st k =h k. Each GO annotation has an associated evidence code that describes how that annotation was produced see for more information on these codes. Some annotation methods are more reliable; thus, these evidence codes allow biologists to weight the reliability of each annotation. However, these evidence codes are not numeric and, thus, cannot be directly applied to fuzzy integrals. Reference [3] proposes a method to encode words as IT2 FSs. This approach aggregates intervals into IT2 FSs, where each FS represents a word The intervals often are the result of a survey of experts. However, we do not yet have an IT2 fuzzy-valued fuzzy measure; we have a paper in preparation that addresses this topic. In [23], the authors associated a numeric weight with each evidence code; Table II contains these values. For each pair of terms T k = {T i,t 2 }, there are two associated evidence codes. We denote the numeric weights from Table II of these evidence codes as ct i and ct 2. Thus the confidence or worth of h k is g k = f ct i,ct 2, 39 where f is some aggregation operation maximum, average, or minimum on ct i and ct 2. We can now use or 3 to compute the entire fuzzy measure g over X. Example 2. GO-based similarity of two gene products This example computes the similarity of two gene products in the GP D94 data set [3] and, for comparison, mimics Example 4 in [23]. Consider the two gene products, G =
6 TABLE III GENE ONTOLOGY ANNOTATIONS OF GENE PRODUCTS IN EXAMPLE 2 GO Term Definition Evidence Gene product: AAH3569 GO:472 protein phosphatase activity TAS GO:647 protein amino acid dephosphorylation IEA GO:827 zinc ion binding NR Gene product: AAH2399 GO:472 protein phosphatase activity ISS GO:647 protein amino acid dephosphorylation NAS GO:6787 hydrolase activity NR a - Hx π, Hxπ2, Hxπ3, Hx π4 AAH3569 MTMR4 gene and G 2 = AAH2399 MTMR8 gene. The GO annotations and associated evidence codes of these gene products were extracted from the GO on December, 23. Table III contains the annotations of these gene products. The associated weights of G and G 2 are ct = {,.6,.} and ct 2 ={.8,.4,.}. The densities were computed by g k =min{ct i,ct 2 }, min {ct i,ct 2 } = The number-valued pair-wise GO term similarity matrix used in [23] is st i,t 2 = Thus, the sorted evidence are {h πi } = {st k } = {,,,,.,.,.33,.52,.58}, and the respective densities are {g i } = {.,.,.,.,.6,.4,.4,.8,.}. For our example, we picked IT2 FSs that represent the sorted evidence. These sets are shown in Fig. 5. In practice, one could use the methods described in [3] to aggregate intervals, created from multiple GO-based similarity measures, into IT2 FSs. We do not specifically address this in this paper. Figure 6 shows the resulting GO-based similarity of the gene products computed with the extended fuzzy Choquet integral proposed in this paper. View a shows the result using the decomposable fuzzy measure in Eq. 3. Note that for this measure the α =level-cut α= C g UMF H =.5, which is the same result that the number-valued Choquet integral similarity measure produced in [23]. View b shows the result using the fuzzy λ-measure. We prefer the λ- measure for real applications of the fuzzy Choquet integral as this measure adapts well to densities that have a sum greater than. V. CONCLUSION This paper proposed a fuzzy Choquet integral for an interval type-2 fuzzy number-valued evidence. We showed that the bounds of the footprint-of-uncertainty of the result could be calculated by computing the integral on the upper and lower membership functions of the interval type-2 evidence. This is an important aspect as this implementation b. - Hx π5, Hx π c.33 - Hx π d.52 - Hx π e.58 - Hx π9 Fig. 5. Interval type-2 FNs that represent the evidence of GO-based similarity of two gene products.
7 a Decomposable fuzzy measure, Eq b Fuzzy λ-measure Fig. 6. GO-based similarity of gene products, AAH3569 and AAH2399, computed with extended fuzzy Choquet integral and two different fuzzy measures. Red shows the evidence and green is the result. could be used in real applications with little additional computational overhead compared to a type- fuzzy Choquet integral. In fact, the big-oh complexity of the algorithms are equivalent. In the second example we extended the Gene Ontologybased similarity measure proposed in [23]. This shows that our method has a practical application. We also would like to emphasize that this shows future merit of using the Choquet integral with type-2 fuzzy numbers. In the Gene Ontology example, the worth of the evidence was expressed as linguistic evidence codes, to which we applied a numeric weight. We are currently surveying experts to produce interval type- 2 fuzzy numbers that represent each evidence code. We are also extending fuzzy measures to type-2 representations. This discussion leads to the prognostication of a generalized fuzzy integral and fuzzy measure that can address type-2 fuzzy numbers. Table IV illustrates how our proposed Choquet integral for interval type-2 fuzzy integrands is an instantiation of a linguistic fuzzy integral LFI ; our implementation is a LFI for a number-valued fuzzy measure and an interval type-2 fuzzy number-valued integrand evidence. We are currently working on the development of fuzzy integrals and fuzzy measures that fall within all the LFI cells of Table IV. The challenges in this regard include developing interval-valued, fuzzy number-valued, and interval type-2 fuzzy number-valued fuzzy measures. For example, how are fuzzy measure properties, enumerated at the beginning of this paper, extended to intervals, fuzzy numbers, and interval We follow the naming convention in [2, 22], where the authors proposed a linguistic weighted average. TABLE IV BREAKDOWN OF DIFFERENT TYPES OF FUZZY INTEGRALS; FI-FUZZY INTEGRAL, IFI-INTERVAL FUZZY INTEGRAL, FFI-FUZZY FUZZY INTEGRAL, LFI-LINGUISTIC FUZZY INTEGRAL Fuzzy Measure Evidence Numbers Intervals T FSs IT2 FSs Numbers FI IFI FFI LFI Intervals IFI IFI FFI LFI T FNs FFI FFI FFI LFI IT2 FNs LFI LFI LFI LFI type-2 fuzzy numbers? Interval-valued and fuzzy numbervalued fuzzy measures have been discussed [32, 33], but to our knowledge interval type-2 fuzzy measures have not. In conclusion, we propose LFIs as an instantiation of computing with words. Fuzzy integrals have been shown to be useful for information fusion [4 6], and we believe that the linguistic forms will be useful for fusion of evidence that is linguistic in nature. ACKNOWLEDGEMENT Timothy Havens and James Keller were supported in part by grants from the Leonard Wood Institute LWI and Army Research Office EV in support of the U.S. Army RDECOM CERDEC NVESD. Derek Anderson is a pre-doctoral biomedical informatics research fellow funded by the National Library of Medicine T5 LM789. REFERENCES [] M. Grabisch, Ed., Fuzzy Measures and Integrals: Theory and Applications. New York: Physica-Verlag, 2. [2] M. Sugeno, Fuzzy Automata and Decision Processes. New York: North-Holland, 977, ch. Fuzzy measures and fuzzy integrals: a survey, pp [3] D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications. New York: Academic Press, 96. [4] M. Grabisch, Fuzzy Measures and Integrals: Theory and Applications. New York: Physica-Verlag, 2, ch. Fuzzy integral for classification and feature extraction, pp [5] J. Keller, P. Gader, and A. Hocaoglu, Fuzzy Measures and Integrals: Theory and Applications. New York: Physica-Verlag, 2, ch. Fuzzy integral in image processing and recognition, pp [6] S. Auephanwiriyakul, J. Keller, and P. Gader, Generalized Choquet fuzzy integral fusion, Information Fusion, vol. 3, p. 69, 22. [7] G. Choquet, Theory of capacities, Analles de l Institit Fourier, vol. 5, pp , 953. [8] T. Murofushi and M. Sugeno, An interpretation of fuzzy measure and the choquet integral as an integral with respect to a fuzzy measure, Fuzzy Sets and Systems, vol. 29, pp , 989.
8 [9] D. Zhang and Z. Wang, Fuzzy integrals of fuzzy valued functions, Fuzzy Sets and Systems, vol. 54, pp , 993. [] M. Grabisch, H. Nguyen, and E. Walker, Fundamentals of Uncertainty Calculi, With Applications to Fuzzy Inference. Dordrecht: Kluwer Academic, 995. [] R. Yang, Z. Wang, P. Heng, and K. Leung, Fuzzified Choquet integral with a fuzzy-valued integrand and its application on temperature prediction, IEEE Trans. SMC-B, vol. 38, no. 2, pp , Apr. 28. [2] G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications. Upper Saddle River, New Jersey: Prentice Hall, 995. [3] L. Zadeh, From computing with numbers to computing with words from manipulation of measurements to manipulation of perceptions, Int. J. Appl. Math. Comput. Sci., vol. 2, no. 3, pp , 22. [4] J. Mendel, The perceptual computer: an architecture for computing with words, in Proc. of FUZZ-IEEE, Melbourne, Australia, Dec. 2, pp [5] J. Mendel, Computing with words and its relationships with fuzzistics, Information Sciences, vol. 77, no. 4, pp , Feb. 27. [6] J. Mendel, Computing with words: Zadeh, Turing, Popper, and Occam, IEEE Computational Intelligence Magazine, vol. 2, no. 4, pp. 7, Nov. 27. [7] L. Zadeh, The concept of a linguistic variable and its application to approximate reasoning-, Inform. Sci., vol. 8, pp , 975. [8] J. Mendel and R. John, Footprint of uncertainty and its importance to type-2 fuzzy sets, in Proc. ICAISC, Banff, Canada, Jul. 22, pp [9] J. Mendel, R. John, and F. Liu, Interval type-2 fuzzy logic systems made simple, IEEE Trans. Fuzzy Systems, vol. 4, no. 6, pp , Dec. 26. [2] J. Mendel and R. John, Type-2 fuzzy sets made simple, IEEE Trans. Fuzzy Systems, vol., no. 2, pp. 7 27, Apr. 22. [2] D. Wu and J. Mendel, Aggregation using the linguistic weighted average and interval type-2 fuzzy sets, IEEE Trans. Fuzzy Systems, vol. 5, no. 6, pp. 45 6, Dec. 27. [22] D. Wu and J. Mendel, The linguistic weighted average, in Proc. FUZZ-IEEE, Vancouver, Canada, Jul. 26, pp [23] M. Popescu, J. Keller, and J. Mitchell, Fuzzy measures on the Gene Ontology for gene product similarity, IEEE Trans. on Computational Biology and Bioinformatics, vol. 3, no. 3, pp , 26. [24] The Gene Ontology Consortium, The Gene Ontology GO database and informatics resource, Nucleic Acids Res., vol. 32, pp. D258 D26, 24. [25] P. Resnik, Using information content to evaluate semantic similarity in a taxonomy, in International Joint Conference for Artificial Intelligence, 995, pp [26] D. Lin, An information-theoretic definition of similarity, in Proc. 5th International Conf. on Maching Learning. San Francisco, CA: Morgan Kaufmann, 998, pp [27] J. Jiang and D. Conrath, Semantic similarity based on corpus statistics and lexical ontology. in Proc. of Int. Conf. Res. on Comp. Linguistics X, Taiwan, 997. [28] C. Leacock and M. Chodorow, WordNet: An electronic lexical database. MIT Press, 998, ch. Combining local context and WordNet similarity for word sense identification, pp [29] Z. Wu and M. Palmer, Verb semantics and lexical selection, in Proc. 32nd Annual Meeting of the Association for Computational Linguistics, 994, pp [3] F. Liu and J. Mendel, Encoding words into interval type-2 fuzzy sets using an interval approach, IEEE Trans. Fuzzy Systems, vol. 6, no. 6, pp , Dec. 28. [3] M. Popescu, J. Keller, J. Mitchell, and J. Bezdek, Functional summarization of gene product clusters using Gene Ontology similarity measures, in Proc. 24 ISSNIP. Piscataway, NJ: IEEE Press, 24, pp [32] C. Guo, D. Zhang, and C. Wu, Fuzzy-valued measures and generalized fuzzy integrals, Fuzzy Sets and Systems, vol. 97, pp , 998. [33] D. Anderson, J. Keller, and T. Havens, Learning fuzzyvalued fuzzy measures for the fuzzy-valued Sugeno fuzzy integral, in Proc. IPMU, Dortmund, Germany, 2.
The Arithmetic Recursive Average as an Instance of the Recursive Weighted Power Mean
The Arithmetic Recursive Average as an Instance of the Recursive Weighted Power Mean Christian Wagner Inst. for Computing & Cybersystems Michigan Technological University, USA and LUCID, School of Computer
More informationBECAUSE this paper is a continuation of [9], we assume
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 15, NO. 2, APRIL 2007 301 Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 2, Inverse Problems Jerry M. Mendel, Life Fellow, IEEE, and Hongwei Wu,
More informationSPFI: Shape-Preserving Choquet Fuzzy Integral for Non-Normal Fuzzy Set-Valued Evidence
SPFI: Shape-Preserving hoquet Fuzzy Integral for Non-Normal Fuzzy Set-Valued Evidence Timothy. Havens, nthony J. Pinar Department of Electrical and omputer Engineering Department of omputer Science Michigan
More informationAccess from the University of Nottingham repository:
Tomlin, Leary and Anderson, Derek T. and Wagner, Christian and Havens, Timothy C. and Keller, James M. (2016) Fuzzy integral for rule aggregation in fuzzy inference systems. Communications in Computer
More informationData-Informed Fuzzy Measures for Fuzzy Integration of Intervals and Fuzzy Numbers
Data-Informed Fuzzy Measures for Fuzzy Integration of Intervals and Fuzzy Numbers Timothy C. Havens, Senior Member, IEEE, Derek T. Anderson, Senior Member, IEEE, and Christian Wagner, Senior Member, IEEE
More informationIEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 14, NO. 6, DECEMBER Many methods have been developed for doing this for type-1 fuzzy sets (e.g.
IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 14, NO. 6, DECEMBER 2006 781 Type-2 Fuzzistics for Symmetric Interval Type-2 Fuzzy Sets: Part 1, Forward Problems Jerry M. Mendel, Le Fellow, IEEE, and Hongwei
More informationApplication of Fuzzy Measure and Fuzzy Integral in Students Failure Decision Making
IOSR Journal of Mathematics (IOSR-JM) e-issn: 2278-5728, p-issn: 2319-765X. Volume 10, Issue 6 Ver. III (Nov - Dec. 2014), PP 47-53 Application of Fuzzy Measure and Fuzzy Integral in Students Failure Decision
More informationVariations of non-additive measures
Variations of non-additive measures Endre Pap Department of Mathematics and Informatics, University of Novi Sad Trg D. Obradovica 4, 21 000 Novi Sad, Serbia and Montenegro e-mail: pape@eunet.yu Abstract:
More information2 GENE FUNCTIONAL SIMILARITY. 2.1 Semantic values of GO terms
Bioinformatics Advance Access published March 7, 2007 The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
More informationIntersection and union of type-2 fuzzy sets and connection to (α 1, α 2 )-double cuts
EUSFLAT-LFA 2 July 2 Aix-les-Bains, France Intersection and union of type-2 fuzzy sets and connection to (α, α 2 )-double cuts Zdenko Takáč Institute of Information Engineering, Automation and Mathematics
More informationConstraints Preserving Genetic Algorithm for Learning Fuzzy Measures with an Application to Ontology Matching
Constraints Preserving Genetic Algorithm for Learning Fuzzy Measures with an Application to Ontology Matching Mohammad Al Boni 1, Derek T. Anderson 2, and Roger L. King 3 1,3 Center for Advanced Vehicular
More informationMembership Functions Representing a Number vs. Representing a Set: Proof of Unique Reconstruction
Membership Functions Representing a Number vs. Representing a Set: Proof of Unique Reconstruction Hung T. Nguyen Department of Mathematical Sciences New Mexico State University Las Cruces, New Mexico 88008,
More informationtype-2 fuzzy sets, α-plane, intersection of type-2 fuzzy sets, union of type-2 fuzzy sets, fuzzy sets
K Y B E R N E T I K A V O L U M E 4 9 ( 2 3 ), N U M B E R, P A G E S 4 9 6 3 ON SOME PROPERTIES OF -PLANES OF TYPE-2 FUZZY SETS Zdenko Takáč Some basic properties of -planes of type-2 fuzzy sets are investigated
More informationTYPE-2 fuzzy sets (T2 FSs), originally introduced by
808 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 14, NO. 6, DECEMBER 2006 Interval Type-2 Fuzzy Logic Systems Made Simple Jerry M. Mendel, Life Fellow, IEEE, Robert I. John, Member, IEEE, and Feilong Liu,
More informationA New Method to Forecast Enrollments Using Fuzzy Time Series
International Journal of Applied Science and Engineering 2004. 2, 3: 234-244 A New Method to Forecast Enrollments Using Fuzzy Time Series Shyi-Ming Chen a and Chia-Ching Hsu b a Department of Computer
More informationType-2 Fuzzy Logic Control of Continuous Stirred Tank Reactor
dvance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 3, Number 2 (2013), pp. 169-178 Research India Publications http://www.ripublication.com/aeee.htm Type-2 Fuzzy Logic Control of Continuous
More informationInterval based Uncertain Reasoning using Fuzzy and Rough Sets
Interval based Uncertain Reasoning using Fuzzy and Rough Sets Y.Y. Yao Jian Wang Department of Computer Science Lakehead University Thunder Bay, Ontario Canada P7B 5E1 Abstract This paper examines two
More informationFuzzy relation equations with dual composition
Fuzzy relation equations with dual composition Lenka Nosková University of Ostrava Institute for Research and Applications of Fuzzy Modeling 30. dubna 22, 701 03 Ostrava 1 Czech Republic Lenka.Noskova@osu.cz
More informationis implemented by a fuzzy relation R i and is defined as
FS VI: Fuzzy reasoning schemes R 1 : ifx is A 1 and y is B 1 then z is C 1 R 2 : ifx is A 2 and y is B 2 then z is C 2... R n : ifx is A n and y is B n then z is C n x is x 0 and y is ȳ 0 z is C The i-th
More informationExtended Triangular Norms on Gaussian Fuzzy Sets
EUSFLAT - LFA 005 Extended Triangular Norms on Gaussian Fuzzy Sets Janusz T Starczewski Department of Computer Engineering, Częstochowa University of Technology, Częstochowa, Poland Department of Artificial
More informationCHAPTER V TYPE 2 FUZZY LOGIC CONTROLLERS
CHAPTER V TYPE 2 FUZZY LOGIC CONTROLLERS In the last chapter fuzzy logic controller and ABC based fuzzy controller are implemented for nonlinear model of Inverted Pendulum. Fuzzy logic deals with imprecision,
More informationType-2 Fuzzy Alpha-cuts
1 Type-2 Fuzzy Alpha-cuts Hussam Hamrawi College of Computer Sciences University of Bahri Khartoum, Sudan Email: hussamw@bahri.edu.sd Simon Coupland Centre for Computational Intelligence De Montfort University
More informationISSN: Received: Year: 2018, Number: 24, Pages: Novel Concept of Cubic Picture Fuzzy Sets
http://www.newtheory.org ISSN: 2149-1402 Received: 09.07.2018 Year: 2018, Number: 24, Pages: 59-72 Published: 22.09.2018 Original Article Novel Concept of Cubic Picture Fuzzy Sets Shahzaib Ashraf * Saleem
More informationEfficient Binary Fuzzy Measure Representation and Choquet Integral Learning
Efficient Binary Fuzzy Measure Representation and Choquet Integral Learning M. Islam 1 D. T. Anderson 2 X. Du 2 T. Havens 3 C. Wagner 4 1 Mississippi State University, USA 2 University of Missouri, USA
More informationComputing with Words: Towards a New Tuple-Based Formalization
Computing with Words: Towards a New Tuple-Based Formalization Olga Kosheleva, Vladik Kreinovich, Ariel Garcia, Felipe Jovel, Luis A. Torres Escobedo University of Teas at El Paso 500 W. University El Paso,
More informationAumann-Shapley Values on a Class of Cooperative Fuzzy Games
Journal of Uncertain Systems Vol.6, No.4, pp.27-277, 212 Online at: www.jus.org.uk Aumann-Shapley Values on a Class of Cooperative Fuzzy Games Fengye Wang, Youlin Shang, Zhiyong Huang School of Mathematics
More informationA Generalized Decision Logic in Interval-set-valued Information Tables
A Generalized Decision Logic in Interval-set-valued Information Tables Y.Y. Yao 1 and Qing Liu 2 1 Department of Computer Science, University of Regina Regina, Saskatchewan, Canada S4S 0A2 E-mail: yyao@cs.uregina.ca
More informationGENERAL AGGREGATION OPERATORS ACTING ON FUZZY NUMBERS INDUCED BY ORDINARY AGGREGATION OPERATORS
Novi Sad J. Math. Vol. 33, No. 2, 2003, 67 76 67 GENERAL AGGREGATION OPERATORS ACTING ON FUZZY NUMBERS INDUCED BY ORDINARY AGGREGATION OPERATORS Aleksandar Takači 1 Abstract. Some special general aggregation
More informationType-2 Fuzzy Sets for Pattern Recognition: The State-of-the-Art
Journal of Uncertain Systems Vol., No.3, pp.63-77, 27 Online at: www.jus.org.uk Type-2 Fuzzy Sets for Pattern Recognition: The State-of-the-Art Jia Zeng and Zhi-Qiang Liu School of Creative Media, City
More informationON LIU S INFERENCE RULE FOR UNCERTAIN SYSTEMS
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems Vol. 18, No. 1 (2010 1 11 c World Scientific Publishing Company DOI: 10.1142/S0218488510006349 ON LIU S INFERENCE RULE FOR UNCERTAIN
More informationAN EASY COMPUTATION OF MIN AND MAX OPERATIONS FOR FUZZY NUMBERS
J. Appl. Math. & Computing Vol. 21(2006), No. 1-2, pp. 555-561 AN EASY COMPUTATION OF MIN AND MAX OPERATIONS FOR FUZZY NUMBERS DUG HUN HONG* AND KYUNG TAE KIM Abstract. Recently, Chiu and WangFuzzy sets
More informationAn axiomatic approach of the discrete Sugeno integral as a tool to aggregate interacting criteria in a qualitative framework
An axiomatic approach of the discrete Sugeno integral as a tool to aggregate interacting criteria in a qualitative framework Jean-Luc Marichal Revised version, September 20, 2000 Abstract We present a
More informationInterval Type-2 Fuzzy Logic Systems Made Simple by Using Type-1 Mathematics
Interval Type-2 Fuzzy Logic Systems Made Simple by Using Type-1 Mathematics Jerry M. Mendel University of Southern California, Los Angeles, CA WCCI 2006 1 Outline Motivation Type-2 Fuzzy Sets Interval
More informationTemperature Prediction Using Fuzzy Time Series
IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART B: CYBERNETICS, VOL 30, NO 2, APRIL 2000 263 Temperature Prediction Using Fuzzy Time Series Shyi-Ming Chen, Senior Member, IEEE, and Jeng-Ren Hwang
More informationA NEW CLASS OF FUSION RULES BASED ON T-CONORM AND T-NORM FUZZY OPERATORS
A NEW CLASS OF FUSION RULES BASED ON T-CONORM AND T-NORM FUZZY OPERATORS Albena TCHAMOVA, Jean DEZERT and Florentin SMARANDACHE Abstract: In this paper a particular combination rule based on specified
More informationA New Fuzzy Positive and Negative Ideal Solution for Fuzzy TOPSIS
A New Fuzzy Positive and Negative Ideal Solution for Fuzzy TOPSIS MEHDI AMIRI-AREF, NIKBAKHSH JAVADIAN, MOHAMMAD KAZEMI Department of Industrial Engineering Mazandaran University of Science & Technology
More informationIntroduction to Intelligent Control Part 6
ECE 4951 - Spring 2010 ntroduction to ntelligent Control Part 6 Prof. Marian S. Stachowicz Laboratory for ntelligent Systems ECE Department, University of Minnesota Duluth February 4-5, 2010 Fuzzy System
More informationOn the Relation of Probability, Fuzziness, Rough and Evidence Theory
On the Relation of Probability, Fuzziness, Rough and Evidence Theory Rolly Intan Petra Christian University Department of Informatics Engineering Surabaya, Indonesia rintan@petra.ac.id Abstract. Since
More informationINTELLIGENT CONTROL OF DYNAMIC SYSTEMS USING TYPE-2 FUZZY LOGIC AND STABILITY ISSUES
International Mathematical Forum, 1, 2006, no. 28, 1371-1382 INTELLIGENT CONTROL OF DYNAMIC SYSTEMS USING TYPE-2 FUZZY LOGIC AND STABILITY ISSUES Oscar Castillo, Nohé Cázarez, and Dario Rico Instituto
More informationUncertain Logic with Multiple Predicates
Uncertain Logic with Multiple Predicates Kai Yao, Zixiong Peng Uncertainty Theory Laboratory, Department of Mathematical Sciences Tsinghua University, Beijing 100084, China yaok09@mails.tsinghua.edu.cn,
More informationFORECASTING OF ECONOMIC QUANTITIES USING FUZZY AUTOREGRESSIVE MODEL AND FUZZY NEURAL NETWORK
FORECASTING OF ECONOMIC QUANTITIES USING FUZZY AUTOREGRESSIVE MODEL AND FUZZY NEURAL NETWORK Dusan Marcek Silesian University, Institute of Computer Science Opava Research Institute of the IT4Innovations
More informationFUZZY ARITHMETIC BASED LYAPUNOV SYNTHESIS IN THE DESIGN OF STABLE FUZZY CONTROLLERS: A COMPUTING WITH WORDS APPROACH
Int. J. Appl. Math. Comput. Sci., 2002, Vol.12, No.3, 411 421 FUZZY ARITHMETIC BASED LYAPUNOV SYNTHESIS IN THE DESIGN OF STABLE FUZZY CONTROLLERS: A COMPUTING WITH WORDS APPROACH CHANGJIU ZHOU School of
More informationA Complete Description of Comparison Meaningful Functions
A Complete Description of Comparison Meaningful Functions Jean-Luc MARICHAL Radko MESIAR Tatiana RÜCKSCHLOSSOVÁ Abstract Comparison meaningful functions acting on some real interval E are completely described
More informationFrancisco M. Couto Mário J. Silva Pedro Coutinho
Francisco M. Couto Mário J. Silva Pedro Coutinho DI FCUL TR 03 29 Departamento de Informática Faculdade de Ciências da Universidade de Lisboa Campo Grande, 1749 016 Lisboa Portugal Technical reports are
More informationFeature Selection with Fuzzy Decision Reducts
Feature Selection with Fuzzy Decision Reducts Chris Cornelis 1, Germán Hurtado Martín 1,2, Richard Jensen 3, and Dominik Ślȩzak4 1 Dept. of Mathematics and Computer Science, Ghent University, Gent, Belgium
More informationCompenzational Vagueness
Compenzational Vagueness Milan Mareš Institute of information Theory and Automation Academy of Sciences of the Czech Republic P. O. Box 18, 182 08 Praha 8, Czech Republic mares@utia.cas.cz Abstract Some
More informationOn (Weighted) k-order Fuzzy Connectives
Author manuscript, published in "IEEE Int. Conf. on Fuzzy Systems, Spain 2010" On Weighted -Order Fuzzy Connectives Hoel Le Capitaine and Carl Frélicot Mathematics, Image and Applications MIA Université
More informationWhy Trapezoidal and Triangular Membership Functions Work So Well: Towards a Theoretical Explanation
Journal of Uncertain Systems Vol.8, No.3, pp.164-168, 2014 Online at: www.jus.org.uk Why Trapezoidal and Triangular Membership Functions Work So Well: Towards a Theoretical Explanation Aditi Barua, Lalitha
More informationHow to Define "and"- and "or"-operations for Intuitionistic and Picture Fuzzy Sets
University of Texas at El Paso DigitalCommons@UTEP Departmental Technical Reports (CS) Computer Science 3-2019 How to Define "and"- and "or"-operations for Intuitionistic and Picture Fuzzy Sets Christian
More informationAn Approach to Classification Based on Fuzzy Association Rules
An Approach to Classification Based on Fuzzy Association Rules Zuoliang Chen, Guoqing Chen School of Economics and Management, Tsinghua University, Beijing 100084, P. R. China Abstract Classification based
More informationTowards Decision Making under General Uncertainty
University of Texas at El Paso DigitalCommons@UTEP Departmental Technical Reports (CS) Department of Computer Science 3-2017 Towards Decision Making under General Uncertainty Andrzej Pownuk University
More informationCONTROL SYSTEMS, ROBOTICS AND AUTOMATION Vol. XVII - Analysis and Stability of Fuzzy Systems - Ralf Mikut and Georg Bretthauer
ANALYSIS AND STABILITY OF FUZZY SYSTEMS Ralf Mikut and Forschungszentrum Karlsruhe GmbH, Germany Keywords: Systems, Linear Systems, Nonlinear Systems, Closed-loop Systems, SISO Systems, MISO systems, MIMO
More informationData Retrieval and Noise Reduction by Fuzzy Associative Memories
Data Retrieval and Noise Reduction by Fuzzy Associative Memories Irina Perfilieva, Marek Vajgl University of Ostrava, Institute for Research and Applications of Fuzzy Modeling, Centre of Excellence IT4Innovations,
More informationChebyshev Type Inequalities for Sugeno Integrals with Respect to Intuitionistic Fuzzy Measures
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 9, No 2 Sofia 2009 Chebyshev Type Inequalities for Sugeno Integrals with Respect to Intuitionistic Fuzzy Measures Adrian I.
More informationNon Additive Measures for Group Multi Attribute Decision Models
Non Additive Measures for Group Multi Attribute Decision Models Marta Cardin, Silvio Giove Dept. of Applied Mathematics, University of Venice Venice, Italy Email: mcardin@unive.it, sgiove@unive.it Abstract
More informationOn Riesz-Fischer sequences and lower frame bounds
On Riesz-Fischer sequences and lower frame bounds P. Casazza, O. Christensen, S. Li, A. Lindner Abstract We investigate the consequences of the lower frame condition and the lower Riesz basis condition
More informationApplication of Fuzzy Abduction Technique in Aerospace Dynamics
Vol 7, No 5, 2016 Application of Fuzzy Abduction Technique in Aerospace Dynamics Sudipta Ghosh Department of Electronics and Communication Engineering Calcutta Institute of Engineering and Management Souvik
More informationChapter 1 Similarity Based Reasoning Fuzzy Systems and Universal Approximation
Chapter 1 Similarity Based Reasoning Fuzzy Systems and Universal Approximation Sayantan Mandal and Balasubramaniam Jayaram Abstract In this work, we show that fuzzy inference systems based on Similarity
More informationComputations Under Time Constraints: Algorithms Developed for Fuzzy Computations can Help
Journal of Uncertain Systems Vol.6, No.2, pp.138-145, 2012 Online at: www.jus.org.uk Computations Under Time Constraints: Algorithms Developed for Fuzzy Computations can Help Karen Villaverde 1, Olga Kosheleva
More informationAllocation of Resources in CB Defense: Optimization and Ranking. NMSU: J. Cowie, H. Dang, B. Li Hung T. Nguyen UNM: F. Gilfeather Oct 26, 2005
Allocation of Resources in CB Defense: Optimization and Raning by NMSU: J. Cowie, H. Dang, B. Li Hung T. Nguyen UNM: F. Gilfeather Oct 26, 2005 Outline Problem Formulation Architecture of Decision System
More informationType-2 Fuzzy Alpha-cuts
Type-2 Fuzzy Alpha-cuts Hussam Hamrawi, BSc. MSc. Submitted in partial fulfilment of the requirements for the degree of PhD. at De Montfort University April, 2011 Acknowledgment Overall, I would like to
More informationType-2 Fuzzy Shortest Path
Intern. J. Fuzzy Mathematical rchive Vol. 2, 2013, 36-42 ISSN: 2320 3242 (P), 2320 3250 (online) Published on 15 ugust 2013 www.researchmathsci.org International Journal of Type-2 Fuzzy Shortest Path V.
More informationQuasi-Lovász extensions on bounded chains
Quasi-Lovász extensions on bounded chains Miguel Couceiro and Jean-Luc Marichal 1 LAMSADE - CNRS, Université Paris-Dauphine Place du Maréchal de Lattre de Tassigny 75775 Paris Cedex 16, France miguel.couceiro@dauphine.fr
More informationA NOTE ON L-FUZZY BAGS AND THEIR EXPECTED VALUES. 1. Introduction
t m Mathematical Publications DOI: 10.1515/tmmp-2016-0021 Tatra Mt. Math. Publ. 66 (2016) 73 80 A NOTE ON L-FUZZY BAGS AND THEIR EXPECTED VALUES Fateme Kouchakinejad Mashaallah Mashinchi Radko Mesiar ABSTRACT.
More informationMulticriteria Decision Making Based on Fuzzy Relations
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 8, No 4 Sofia 2008 Multicriteria Decision Maing Based on Fuzzy Relations Vania Peneva, Ivan Popchev Institute of Information
More informationRepetitive control mechanism of disturbance rejection using basis function feedback with fuzzy regression approach
Repetitive control mechanism of disturbance rejection using basis function feedback with fuzzy regression approach *Jeng-Wen Lin 1), Chih-Wei Huang 2) and Pu Fun Shen 3) 1) Department of Civil Engineering,
More informationTYPE-2 FUZZY G-TOLERANCE RELATION AND ITS PROPERTIES
International Journal of Analysis and Applications ISSN 229-8639 Volume 5, Number 2 (207), 72-78 DOI: 8924/229-8639-5-207-72 TYPE-2 FUZZY G-TOLERANCE RELATION AND ITS PROPERTIES MAUSUMI SEN,, DHIMAN DUTTA
More informationS-MEASURES, T -MEASURES AND DISTINGUISHED CLASSES OF FUZZY MEASURES
K Y B E R N E T I K A V O L U M E 4 2 ( 2 0 0 6 ), N U M B E R 3, P A G E S 3 6 7 3 7 8 S-MEASURES, T -MEASURES AND DISTINGUISHED CLASSES OF FUZZY MEASURES Peter Struk and Andrea Stupňanová S-measures
More informationOn the VC-Dimension of the Choquet Integral
On the VC-Dimension of the Choquet Integral Eyke Hüllermeier and Ali Fallah Tehrani Department of Mathematics and Computer Science University of Marburg, Germany {eyke,fallah}@mathematik.uni-marburg.de
More informationQuasi-Lovász Extensions and Their Symmetric Counterparts
Quasi-Lovász Extensions and Their Symmetric Counterparts Miguel Couceiro, Jean-Luc Marichal To cite this version: Miguel Couceiro, Jean-Luc Marichal. Quasi-Lovász Extensions and Their Symmetric Counterparts.
More informationDivergence measure of intuitionistic fuzzy sets
Divergence measure of intuitionistic fuzzy sets Fuyuan Xiao a, a School of Computer and Information Science, Southwest University, Chongqing, 400715, China Abstract As a generation of fuzzy sets, the intuitionistic
More informationAPPLICATION OF FUZZY LOGIC IN THE CLASSICAL CELLULAR AUTOMATA MODEL
J. Appl. Math. & Computing Vol. 20(2006), No. 1-2, pp. 433-443 Website: http://jamc.net APPLICATION OF FUZZY LOGIC IN THE CLASSICAL CELLULAR AUTOMATA MODEL CHUNLING CHANG, YUNJIE ZHANG, YUNYING DONG Abstract.
More informationResearch Article On Decomposable Measures Induced by Metrics
Applied Mathematics Volume 2012, Article ID 701206, 8 pages doi:10.1155/2012/701206 Research Article On Decomposable Measures Induced by Metrics Dong Qiu 1 and Weiquan Zhang 2 1 College of Mathematics
More informationFuzzy Integral for Classification and Feature Extraction
Fuzzy Integral for Classification and Feature Extraction Michel GRABISCH Thomson-CSF, Corporate Research Laboratory Domaine de Corbeville 91404 Orsay Cedex, France email grabisch@thomson-lcr.fr Abstract
More informationPrevious Accomplishments. Focus of Research Iona College. Focus of Research Iona College. Publication List Iona College. Journals
Network-based Hard/Soft Information Fusion: Soft Information and its Fusion Ronald R. Yager, Tel. 212 249 2047, E-Mail: yager@panix.com Objectives: Support development of hard/soft information fusion Develop
More informationFuzzy Local Trend Transform based Fuzzy Time Series Forecasting Model
Int. J. of Computers, Communications & Control, ISSN 1841-9836, E-ISSN 1841-9844 Vol. VI (2011), No. 4 (December), pp. 603-614 Fuzzy Local Trend Transform based Fuzzy Time Series Forecasting Model J. Dan,
More informationFuzzy Modal Like Approximation Operations Based on Residuated Lattices
Fuzzy Modal Like Approximation Operations Based on Residuated Lattices Anna Maria Radzikowska Faculty of Mathematics and Information Science Warsaw University of Technology Plac Politechniki 1, 00 661
More informationFuzzy Systems. Possibility Theory.
Fuzzy Systems Possibility Theory Rudolf Kruse Christian Moewes {kruse,cmoewes}@iws.cs.uni-magdeburg.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Department of Knowledge Processing
More informationGroup Decision Making Using Comparative Linguistic Expression Based on Hesitant Intuitionistic Fuzzy Sets
Available at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 932-9466 Vol. 0, Issue 2 December 205), pp. 082 092 Applications and Applied Mathematics: An International Journal AAM) Group Decision Making Using
More informationA new Approach to Drawing Conclusions from Data A Rough Set Perspective
Motto: Let the data speak for themselves R.A. Fisher A new Approach to Drawing Conclusions from Data A Rough et Perspective Zdzisław Pawlak Institute for Theoretical and Applied Informatics Polish Academy
More informationIndex Terms Magnetic Levitation System, Interval type-2 fuzzy logic controller, Self tuning type-2 fuzzy controller.
Comparison Of Interval Type- Fuzzy Controller And Self Tuning Interval Type- Fuzzy Controller For A Magnetic Levitation System Shabeer Ali K P 1, Sanjay Sharma, Dr.Vijay Kumar 3 1 Student, E & CE Department,
More informationTuning of Fuzzy Systems as an Ill-Posed Problem
Tuning of Fuzzy Systems as an Ill-Posed Problem Martin Burger 1, Josef Haslinger 2, and Ulrich Bodenhofer 2 1 SFB F 13 Numerical and Symbolic Scientific Computing and Industrial Mathematics Institute,
More informationFusing Interval Preferences
Fusing Interval Preferences Taner Bilgiç Department of Industrial Engineering Boğaziçi University Bebek, İstanbul, 80815 Turkey taner@boun.edu.tr Appeared in Proceedings of EUROFUSE Workshop on Preference
More informationData Dependence in Combining Classifiers
in Combining Classifiers Mohamed Kamel, Nayer Wanas Pattern Analysis and Machine Intelligence Lab University of Waterloo CANADA ! Dependence! Dependence Architecture! Algorithm Outline Pattern Recognition
More informationHierarchical Structures on Multigranulation Spaces
Yang XB, Qian YH, Yang JY. Hierarchical structures on multigranulation spaces. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 27(6): 1169 1183 Nov. 2012. DOI 10.1007/s11390-012-1294-0 Hierarchical Structures
More informationDistinguishing Causes from Effects using Nonlinear Acyclic Causal Models
JMLR Workshop and Conference Proceedings 6:17 164 NIPS 28 workshop on causality Distinguishing Causes from Effects using Nonlinear Acyclic Causal Models Kun Zhang Dept of Computer Science and HIIT University
More informationFuzzy system reliability analysis using time dependent fuzzy set
Control and Cybernetics vol. 33 (24) No. 4 Fuzzy system reliability analysis using time dependent fuzzy set by Isbendiyar M. Aliev 1 and Zohre Kara 2 1 Institute of Information Technologies of National
More informationQuantification of Perception Clusters Using R-Fuzzy Sets and Grey Analysis
Quantification of Perception Clusters Using R-Fuzzy Sets and Grey Analysis Arab Singh Khuman, Yingie Yang Centre for Computational Intelligence (CCI) School of Computer Science and Informatics De Montfort
More informationOn flexible database querying via extensions to fuzzy sets
On flexible database querying via extensions to fuzzy sets Guy de Tré, Rita de Caluwe Computer Science Laboratory Ghent University Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium {guy.detre,rita.decaluwe}@ugent.be
More informationFuzzy Systems. Introduction
Fuzzy Systems Introduction Prof. Dr. Rudolf Kruse Christoph Doell {kruse,doell}@iws.cs.uni-magdeburg.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Department of Knowledge Processing
More informationWhy Bellman-Zadeh Approach to Fuzzy Optimization
Applied Mathematical Sciences, Vol. 12, 2018, no. 11, 517-522 HIKARI Ltd, www.m-hikari.com https://doi.org/10.12988/ams.2018.8456 Why Bellman-Zadeh Approach to Fuzzy Optimization Olga Kosheleva 1 and Vladik
More informationLinguistic-Valued Approximate Reasoning With Lattice Ordered Linguistic-Valued Credibility
International Journal of Computational Intelligence Systems, Vol. 8, No. 1 (2015) 53-61 Linguistic-Valued Approximate Reasoning With Lattice Ordered Linguistic-Valued Credibility Li Zou and Yunxia Zhang
More informationA linguistic fuzzy model with a monotone rule base is not always monotone
EUSFLAT - LFA 25 A linguistic fuzzy model with a monotone rule base is not always monotone Ester Van Broekhoven and Bernard De Baets Department of Applied Mathematics, Biometrics and Process Control Ghent
More informationApproximation Capability of SISO Fuzzy Relational Inference Systems Based on Fuzzy Implications
Approximation Capability of SISO Fuzzy Relational Inference Systems Based on Fuzzy Implications Sayantan Mandal and Balasubramaniam Jayaram Department of Mathematics Indian Institute of Technology Hyderabad
More informationThe problem of distributivity between binary operations in bifuzzy set theory
The problem of distributivity between binary operations in bifuzzy set theory Pawe l Drygaś Institute of Mathematics, University of Rzeszów ul. Rejtana 16A, 35-310 Rzeszów, Poland e-mail: paweldr@univ.rzeszow.pl
More informationProduct of Partially Ordered Sets (Posets), with Potential Applications to Uncertainty Logic and Space-Time Geometry
University of Texas at El Paso DigitalCommons@UTEP Departmental Technical Reports (CS) Department of Computer Science 6-1-2011 Product of Partially Ordered Sets (Posets), with Potential Applications to
More informationThe Limitation of Bayesianism
The Limitation of Bayesianism Pei Wang Department of Computer and Information Sciences Temple University, Philadelphia, PA 19122 pei.wang@temple.edu Abstract In the current discussion about the capacity
More informationMODELLING OF TOOL LIFE, TORQUE AND THRUST FORCE IN DRILLING: A NEURO-FUZZY APPROACH
ISSN 1726-4529 Int j simul model 9 (2010) 2, 74-85 Original scientific paper MODELLING OF TOOL LIFE, TORQUE AND THRUST FORCE IN DRILLING: A NEURO-FUZZY APPROACH Roy, S. S. Department of Mechanical Engineering,
More informationIntelligent Systems and Control Prof. Laxmidhar Behera Indian Institute of Technology, Kanpur
Intelligent Systems and Control Prof. Laxmidhar Behera Indian Institute of Technology, Kanpur Module - 2 Lecture - 4 Introduction to Fuzzy Logic Control In this lecture today, we will be discussing fuzzy
More informationUncertain Entailment and Modus Ponens in the Framework of Uncertain Logic
Journal of Uncertain Systems Vol.3, No.4, pp.243-251, 2009 Online at: www.jus.org.uk Uncertain Entailment and Modus Ponens in the Framework of Uncertain Logic Baoding Liu Uncertainty Theory Laboratory
More information