Fuzzy Systems. Fuzzy Control
|
|
- Francine Barton
- 5 years ago
- Views:
Transcription
1 Fuzzy Systems Fuzzy Control Prof. Dr. Rudolf Kruse Christoph Doell Otto-von-Guericke University of Magdeburg Faculty of Computer Science Institute for Intelligent Cooperating Systems R. Kruse, C. Doell FS Fuzzy Control Part 3 1 / 59
2 Mamdani Control
3 Architecture of a Fuzzy Controller knowledge base fuzzification interface fuzzy decision logic fuzzy defuzzification interface not fuzzy measured values controlled system controller output not fuzzy R. Kruse, C. Doell FS Fuzzy Control Part 3 2 / 59
4 Example: Cartpole Problem (cont.) X 1 is partitioned into 7 fuzzy sets. Support of fuzzy sets: intervals with length 1 4 of whole range X 1. Similar fuzzy partitions for X 2 and Y. Next step: specify rules if ξ 1 is A (1) and... and ξ n is A (n) then η is B, A (1),..., A (n) and B represent linguistic terms corresponding to µ (1),..., µ (n) and µ according to X 1,..., X n and Y. Let the rule base consist of k rules. R. Kruse, C. Doell FS Fuzzy Control Part 3 3 / 59
5 Example: Cartpole Problem (cont.) θ nb nm ns az ps pm pb nb ps pb nm pm ns nm ns ps θ az nb nm ns az ps pm pb ps ns ps pm pm nm pb nb ns 19 rules for cartpole problem, e.g. If θ is approximately zero and θ is negative medium then F is positive medium. R. Kruse, C. Doell FS Fuzzy Control Part 3 4 / 59
6 Definition of Table-based Control Function Measurement (x 1,..., x n ) X 1... X n is forwarded to decision logic. Consider rule if ξ 1 is A (1) and... and ξ n is A (n) then η is B. Decision logic computes degree to ξ 1,..., ξ n fulfills premise of rule. For 1 ν n, the value µ (ν) (x ν ) is calculated. { Combine values conjunctively by α = min µ (1),..., µ (n)}. For each rule R r with 1 r k, compute { α r = min µ (1) i 1,r (x 1 ),..., µ (n) } i n,r (x n ). R. Kruse, C. Doell FS Fuzzy Control Part 3 5 / 59
7 Definition of Table-based Control Function II Output of R r = fuzzy set of output values. Thus cutting off fuzzy set µ ir associated with conclusion of R r at α r. So for input (x 1,..., x n ), R r implies fuzzy set µ output(rr ) x 1,...,x n : Y [0, 1], { y min µ (1) i 1,r (x 1 ),..., µ (n) } i n,r (x n ), µ ir (y). If µ (1) i 1,r (x 1 ) =... = µ (n) i n,r (x n ) = 1, then µ output(rr ) x 1,...,x n = µ ir. If for all ν {1,..., n}, µ (ν) i 1,r (x ν ) = 0, then µ output(rr ) x 1,...,x n = 0. R. Kruse, C. Doell FS Fuzzy Control Part 3 6 / 59
8 Combination of Rules The decision logic combines the fuzzy sets from all rules. The maximum leads to the output fuzzy set µ output x 1,...,x n : Y [0, 1], y max 1 r k { { min µ (1) i 1,r (x 1 ),..., µ (n) }} i n,r (x n ), µ ir (y). Then µ output x 1,...,x n is passed to defuzzification interface. R. Kruse, C. Doell FS Fuzzy Control Part 3 7 / 59
9 Rule Evaluation θ positive small positive medium θ approx. zero approx. zero Rule evaluation for Mamdani-Assilian controller. Input tuple (25, 4) leads to fuzzy output. Crisp output is determined by defuzzification. θ θ min min positive small 1 F positive medium F max F R. Kruse, C. Doell FS Fuzzy Control Part 3 8 / 59
10 Defuzzification So far: mapping between each (n 1,..., n n ) and µ output x 1,...,x n. Output = description of output value as fuzzy set. Defuzzification interface derives crisp value from µ output x 1,...,x n. This step is called defuzzification. Most common methods: max criterion, mean of maxima, center of gravity. R. Kruse, C. Doell FS Fuzzy Control Part 3 9 / 59
11 The Max Criterion Method Choose an arbitrary y Y for which µ output x 1,...,x n reaches the maximum membership value. Advantages: Applicable for arbitrary fuzzy sets. Applicable for arbitrary domain Y (even for Y IR). Disadvantages: Rather class of defuzzification strategies than single method. Which value of maximum membership? Random values and thus non-deterministic controller. Leads to discontinuous control actions. R. Kruse, C. Doell FS Fuzzy Control Part 3 10 / 59
12 The Mean of Maxima (MOM) Method Preconditions: (i) Y is interval (ii) Y Max = {y Y y Y : µ output x 1,...,x n (y ) µ output x 1,...,x n (y)} is non-empty and measurable (iii) Y Max is set of all y Y such that µ output x 1,...,x n is maximal Crisp output value = mean value of Y Max. if Y Max is finite: η = 1 Y Max y i Y Max y i if Y Max is infinite: y Y η = Max y dy y Y Max dy MOM can lead to discontinuous control actions. R. Kruse, C. Doell FS Fuzzy Control Part 3 11 / 59
13 Center of Gravity (COG) Method Same preconditions as MOM method. η = center of gravity/area of µ output x 1,...,x n If Y is finite, then η = y i Y y i µ output x 1,...,x n (y i ). y i Y µoutput x 1,...,x n (y i ) If Y is infinite, then η = y Y y µoutput x 1,...,x n (y) dy y Y µoutput x 1,...,x n (y) dy. R. Kruse, C. Doell FS Fuzzy Control Part 3 12 / 59
14 Center of Gravity (COG) Method Advantages: Nearly always smooth behavior, If certain rule dominates once, not necessarily dominating again. Disadvantage: No semantic justification, Long computation, Counterintuitive results possible. Also called center of area (COA) method: take value that splits µ output x 1,...,x n into 2 equal parts. R. Kruse, C. Doell FS Fuzzy Control Part 3 13 / 59
15 Example Task: compute η COG and η MOM of fuzzy set shown below. Based on finite set Y = 0, 1,..., 10 and infinite set Y = [0, 10] µ output x 1,...,x n (y) η COG η MOM y R. Kruse, C. Doell FS Fuzzy Control Part 3 14 / 59
16 Example for COG Continuous and Discrete Output Space η COG = = η COG = 10 0 y µ output x 1,...,x n (y) dy 10 0 µ output x 1,...,x n (y) dy y dy (0.2y 0.6)y dy y dy ( ) ( ) = R. Kruse, C. Doell FS Fuzzy Control Part 3 15 / 59
17 Example for MOM Continuous and Discrete Output Space η MOM = = 10 7 y dy 10 7 dy = 8.5 = η MOM = = 34 4 = 8.5 R. Kruse, C. Doell FS Fuzzy Control Part 3 16 / 59
18 Problem Case for MOM and COG 1 µ output x 1,...,x n What would be the output of MOM or COG? Is this desirable or not? R. Kruse, C. Doell FS Fuzzy Control Part 3 17 / 59
19 Example: Engine Idle Speed Control VW 2000cc 116hp Motor (Golf GTI) R. Kruse, C. Doell FS Fuzzy Control Part 3 18 / 59
20 Structure of the Fuzzy Controller R. Kruse, C. Doell FS Fuzzy Control Part 3 19 / 59
21 Deviation of the Number of Revolutions drev nb nm ns zr ps pm pb R. Kruse, C. Doell FS Fuzzy Control Part 3 20 / 59
22 Gradient of the Number of Revolutions grev nb nm ns zr ps pm pb R. Kruse, C. Doell FS Fuzzy Control Part 3 21 / 59
23 Change of Current for Auxiliary Air Regulator daarcur nh nb nm ns zr ps pm pb ph R. Kruse, C. Doell FS Fuzzy Control Part 3 22 / 59
24 Rule Base If the deviation from the desired number of revolutions is negative small and the gradient is negative medium, then the change of the current for the auxiliary air regulation should be positive medium. grev nb nm ns az ps pm pb nb ph pb pb pm pm ps ps nm ph pb pm pm ps ps az ns pb pm ps ps az az az drev az ps ps az az az nm ns ps az az az ns ns nm nb pm az ns ns ns nb nb nh pb ns ns nm nb nb nb nh R. Kruse, C. Doell FS Fuzzy Control Part 3 23 / 59
25 Performance Characteristics R. Kruse, C. Doell FS Fuzzy Control Part 3 24 / 59
26 Example: Automatic Gear Box I VW gear box with 2 modes (eco, sport) in series line until Research issue since 1991: individual adaption of set points and no additional sensors. Idea: car watches driver and classifies him/her into calm, normal, sportive (assign sport factor [0, 1]), or nervous (calm down driver). Test car: different drivers, classification by expert (passenger). Simultaneous measurement of 14 attributes, e.g., speed, position of accelerator pedal, speed of accelerator pedal, kick down, steering wheel angle. R. Kruse, C. Doell FS Fuzzy Control Part 3 25 / 59
27 Example: Automatic Gear Box II Continuously Adapting Gear Shift Schedule in VW New Beetle R. Kruse, C. Doell FS Fuzzy Control Part 3 26 / 59
28 Example: Automatic Gear Box III Technical Details Optimized program on Digimat: 24 byte RAM 702 byte ROM uses 7 Mamdani fuzzy rules Runtime: 80 ms 12 times per second new sport factor is assigned. Research topics: When fuzzy control? How to find fuzzy rules? R. Kruse, C. Doell FS Fuzzy Control Part 3 27 / 59
29 Takagi Sugeno Control
30 Takagi-Sugeno Controller Proposed by Tomohiro Takagi and Michio Sugeno. Modification/extension of Mamdani controller. Both in common: fuzzy partitions of input domain X 1,..., X n. Difference to Mamdani controller: no fuzzy partition of output domain Y, controller rules R 1,..., R k are given by R r : if ξ 1 is A (1) i 1,r and... and ξ n is A (n) i n,r then η r = f r (ξ 1,..., ξ n ), f r : X 1... X n Y. Generally, f r is linear, i.e. f r (x 1,..., x n ) = a (r) 0 + n i=1 a (r) i x i. R. Kruse, C. Doell FS Fuzzy Control Part 3 28 / 59
31 Takagi-Sugeno Controller: Conclusion For given input (x 1,..., x n ) and for each R r, decision logic computes truth value α r of each premise, and then f r (x 1,..., x n ). Analogously to Mamdani controller: { α r = min µ (1) i 1,r (x 1 ),..., µ (n) } i n,r (x n ). Output equals crisp control value η = kr=1 α r f r (x 1,..., x n ) kr=1 α r. Thus no defuzzification method necessary. R. Kruse, C. Doell FS Fuzzy Control Part 3 29 / 59
32 Example R 1 : if ξ 1 is then η 1 = 1 ξ ξ R 2 : if ξ 1 is and ξ 2 is then η 2 = 0.1 ξ ξ R 3 : if ξ 1 is and ξ 2 is then η 3 = 0.9 ξ ξ R 4 : if ξ 1 is and ξ 2 is then η 4 = 0.2 ξ ξ If a certain clause x j is A (j) i j,r in rule R r is missing, then µ ij,r (x j ) 1 for all linguistic values i j,r. For instance, here x 2 in R 1, so µ i2,1 (x 2 ) 1 for all i 2,1. R. Kruse, C. Doell FS Fuzzy Control Part 3 30 / 59
33 Example: Output Computation input: (ξ 1, ξ 2 ) = (6, 7) α 1 = 1 /2 1 = 1 /2 η 1 = /2 + 1 = 10.5 α 2 = 1 /2 2 /3 = 1 /2 η 2 = = 28.6 α 3 = 1 /2 1 /3 = 1 /3 η 3 = = 19.3 α 4 = 0 1 /3 = 0 η 4 = /2 + 1 = 10.5 output: η = f (6, 7) = 1 / / / /2 + 1 /2 + 1 /3 = 19.5 R. Kruse, C. Doell FS Fuzzy Control Part 3 31 / 59
34 Example: Passing a Bend Pass a bend with a car at constant speed. Measured inputs: ξ 1 : distance of car to beginning of bend ξ 2 : distance of car to inner barrier ξ 3 : direction (angle) of car ξ 4 : distance of car to outer barrier ξ 4 ξ 3 ξ 2 ξ 1 η = rotation speed of steering wheel X 1 = [0 cm, 150 cm], X 2 = [0 cm, 150 cm] X 3 = [ 90, 90 ], X 4 = [0 cm, 150 cm] R. Kruse, C. Doell FS Fuzzy Control Part 3 32 / 59
35 Fuzzy Partitions of X 1 and X 2 1 small medium big 0 1 small big ξ ξ 2 R. Kruse, C. Doell FS Fuzzy Control Part 3 33 / 59
36 Fuzzy Partitions of X 3 and X 4 outwards 1 forward inwards ξ 3 1 small ξ 4 R. Kruse, C. Doell FS Fuzzy Control Part 3 34 / 59
37 Form of Rules of Car R r : if ξ 1 is A and ξ 2 is B and ξ 3 is C and ξ 4 is D then η = p (A,B,C,D) 0 + p (A,B,C,D) 1 ξ 1 + p (A,B,C,D) 2 ξ 2 + p (A,B,C,D) 3 ξ 3 + p (A,B,C,D) 4 ξ 4 A {small, medium, big} B {small, big} C {outwards, forward, inwards} D {small} p (A,B,C,D) 0,..., p (A,B,C,D) 4 IR R. Kruse, C. Doell FS Fuzzy Control Part 3 35 / 59
38 Control Rules for the Car rule ξ 1 ξ 2 ξ 3 ξ 4 p 0 p 1 p 2 p 3 p 4 R outwards small R forward small R 3 small small outwards R 4 small small forward R 5 small small inwards R 6 small big outwards R 7 small big forward R 8 small big inwards R 9 medium small outwards R 10 medium small forward R 11 medium small inwards R 12 medium big outwards R 13 medium big forward R 14 medium big inwards R 15 big small outwards R 16 big small forward R 17 big small inwards R 18 big big outwards R 19 big big forward R 20 big big inwards R. Kruse, C. Doell FS Fuzzy Control Part 3 36 / 59
39 Sample Calculation Assume that the car is 10 cm away from beginning of bend (ξ 1 = 10). The distance of the car to the inner barrier be 30 cm (ξ 2 = 30). The distance of the car to the outer barrier be 50 cm (ξ 4 = 50). The direction of the car be forward (ξ 3 = 0). Then according to all rules R 1,..., R 20, only premises of R 4 and R 7 have a value 0. R. Kruse, C. Doell FS Fuzzy Control Part 3 37 / 59
40 Membership Degrees to Control Car small medium big ξ 1 = small big ξ 2 = outwards forward inwards ξ 3 = small ξ 4 = 50 0 R. Kruse, C. Doell FS Fuzzy Control Part 3 38 / 59
41 Sample Calculation (cont.) For the premise of R 4 and R 7, α 4 = 1 /4 and α 7 = 1 /6, resp. The rules weights α 4 = 1/4 1/4+1/6 = 3 /5 for R 4 and α 5 = 2 /5 for R 7. R 4 yields R 7 yields η 4 = = η 7 = = The final value for control variable is thus η = 3 / / = R. Kruse, C. Doell FS Fuzzy Control Part 3 39 / 59
42 Fuzzy Control as Similarity-Based reasoning
43 Interpolation in the Presence of Fuzziness Both Takagi-Sugeno and Mamdani are based on heuristics. They are used without a concrete interpretation. Fuzzy control is interpreted as a method to specify a non-linear transition function by knowledge-based interpolation. A fuzzy controller can be interpreted as fuzzy interpolation. Now recall the concept of fuzzy equivalence relations (also called similarity relations). R. Kruse, C. Doell FS Fuzzy Control Part 3 40 / 59
44 Similarity: An Example Specification of a partial control mapping ( good control actions ): gradient deviation R. Kruse, C. Doell FS Fuzzy Control Part 3 41 / 59
45 Interpolation of Control Table There might be additional knowledge available: Some values are indistinguishable, similar or approximately equal. Or they should be treated in a similar way. Two problems: a) How to model information about similarity? b) How to interpolate in case of an existing similarity information? R. Kruse, C. Doell FS Fuzzy Control Part 3 42 / 59
46 How to Model Similarity? Proposal 1: Equivalence Relation Definition Let A be a set and be a binary relation on A. is called an equivalence relation if and only if a, b, c A, (i) a a (reflexivity) (ii) a b b a (symmetry) (iii) a b b c a c (transitivity). Let us try a b a b < ε where ε is fixed. is not transitive, is no equivalence relation. Recall the Poincaré paradox: a b, b c, a c. This is counterintuitive. R. Kruse, C. Doell FS Fuzzy Control Part 3 43 / 59
47 How to Model Similarity? Proposal 2: Fuzzy Equivalence Relation Definition A function E : X 2 [0, 1] is called a fuzzy equivalence relation with respect to the t-norm if it satisfies the following conditions (i) E(x, x) = 1 (reflexivity) x, y, z X (ii) E(x, y) = E(y, x) (symmetry) (iii) (E(x, y), E(y, z)) E(x, z) (t-transitivity). E(x, y) is the degree to which x y holds. E is also called similarity relation, t-equivalence relation, indistinguishability operator, or tolerance relation. Note that property (iii) corresponds to the vague statement if (x y) (y z) then x z. R. Kruse, C. Doell FS Fuzzy Control Part 3 44 / 59
48 Fuzzy Equivalence Relations: An Example Let δ be a pseudo metric on X. Furthermore (a, b) = max{a + b 1, 0} Łukasiewicz t-norm. Then E δ (x, y) = 1 min{δ(x, y), 1} is a fuzzy equivalence relation. δ(x, y) = 1 E δ (x, y) is the induced pseudo metric. Here, fuzzy equivalence and distance are dual notions in this case. Definition A function E : X 2 [0, 1] is called a fuzzy equivalence relation if x, y, z X (i) E(x, x) = 1 (reflexivity) (ii) E(x, y) = E(y, x) (symmetry) (iii) max{e(x, y) + E(y, z) 1, 0} E(x, z) (Łukasiewicz transitivity). R. Kruse, C. Doell FS Fuzzy Control Part 3 45 / 59
49 Fuzzy Sets as Derived Concept δ(x, y) = x y metric E δ (x, y) = 1 min{ x y, 1} fuzzy equivalence relation 1 µ x0 0 x 0 1 x x 0 x µ x0 : X [0, 1] x E δ (x, x 0 ) fuzzy singleton µ x0 describes local similarities. R. Kruse, C. Doell FS Fuzzy Control Part 3 46 / 59
50 Extensional Hull E : IR IR [0, 1], (x, y) 1 min{ x y, 1} is fuzzy equivalence relation w.r.t. Łuka. Definition Let E be a fuzzy equivalence relation on X w.r.t.. µ F(X) is extensional if and only if x, y X : (µ(x), E(x, y)) µ(y). Definition Let E be a fuzzy equivalence relation on a set X. Then the extensional hull of a set M X is the fuzzy set µ M : X [0, 1], x sup{e(x, y) y M}. The extensional hull of {x 0 } is called a singleton. R. Kruse, C. Doell FS Fuzzy Control Part 3 47 / 59
51 Specification of Fuzzy Equivalence Relation Given a family of fuzzy sets that describes local similarities. 1 µ 1 µ 2 µ 3 µ 4 µ 5 µ 6 µ 7 µ 8 µ X There exists a fuzzy equivalence relation on X with induced singletons µ i if and only if i, j : sup{µ i (x) + µ j (x) 1} inf {1 µ i(y) µ j (y) }. x X y X If µ i (x) + µ j (x) 1 for i j, then there is a fuzzy equivalence relation E on X E(x, y) = inf i I {1 µ i(x) µ i (y) }. R. Kruse, C. Doell FS Fuzzy Control Part 3 48 / 59
52 Necessity of Scaling I Are there other fuzzy equivalence relations on IR than E(x, y) = 1 min{ x y, 1}? Integration of scaling. A fuzzy equivalence relation depends on the measurement unit, e.g. Celsius: E(20 C, 20.5 C) = 0.5, Fahrenheit: E(68 F, 68.9 F) = 0.9, scaling factor for Celsius/Fahrenheit = 1.8 (F = 9/5C + 32). E(x, y) = 1 min{ c x c y, 1} is a fuzzy equivalence relation! R. Kruse, C. Doell FS Fuzzy Control Part 3 49 / 59
53 Necessity of Scaling II How to generalize scaling concept? X = [a, b]. Scaling c : X [0, ). Transformation f : X [0, ), x x a c(t)dt. Fuzzy equivalence relation E : X X [0, 1], (x, y) 1 min{ f (x) f (y), 1}. R. Kruse, C. Doell FS Fuzzy Control Part 3 50 / 59
54 Fuzzy Equivalence Relations: Fuzzy Control The imprecision of measurements is modeled by a fuzzy equivalence relations E 1,..., E n and F on X 1,..., X n and Y, resp. The information provided by control expert are k input-output tuples (x (r) 1,..., x n (r), y (r) ) and the description of the fuzzy equivalence relations for input and output spaces, resp. The goal is to derive a control function ϕ : X 1... X n Y from this information. R. Kruse, C. Doell FS Fuzzy Control Part 3 51 / 59
55 Determine Fuzzy-valued Control Functions I The extensional hull of graph of ϕ must be determined. Then the equivalence relation on X 1... X n Y is E((x 1,..., x n, y), (x 1,..., x n, y )) = min{e 1 (x 1, x 1),..., E n (x n, x n), F(y, y )}. R. Kruse, C. Doell FS Fuzzy Control Part 3 52 / 59
56 Determine Fuzzy-valued Control Functions II For X i and Y, define the sets X (0) { i = x X i r {1,..., k} : x = x (r) } i and Y (0) = { y Y r {1,..., k} : y = y (r)}. X (0) i and Y (0) contain all values of the r input-output tuples (x (r) 1,..., x (r) n, y (r) ). For each x 0 X (0) i, singleton µ x0 is obtained by µ x0 (x) = E i (x, x 0 ). R. Kruse, C. Doell FS Fuzzy Control Part 3 53 / 59
57 Determine Fuzzy-valued Control Functions III If ϕ is only partly given, then use E 1,..., E n, F to fill the gaps of ϕ 0. The extensional hull of ϕ 0 is a fuzzy set µ ϕ0 (x 1,..., x n, y ) { = max min{e 1 (x (r) } 1, x 1),..., E n (x n (r), x n), F(y (r), y )}. r {1,...,k} µ ϕ0 is the smallest fuzzy set containing the graph of ϕ 0. Obviously, µ ϕ0 µ ϕ µ (x 1,...,x n) ϕ 0 : Y [0, 1], y µ ϕ0 (x 1,..., x n, y). R. Kruse, C. Doell FS Fuzzy Control Part 3 54 / 59
58 Reinterpretation of Mamdani Controller For input (x 1,..., x n ), the projection of the extensional hull of graph of ϕ 0 leads to a fuzzy set as output. This is identical to the Mamdani controller output. It identifies the input-output tuples of ϕ 0 by linguistic rules: R r : if X 1 is approximately x (r) 1 and... and X n is approximately x (r) n then Y is y (r). A fuzzy controller based on equivalence relations behaves like a Mamdani controller. R. Kruse, C. Doell FS Fuzzy Control Part 3 55 / 59
59 Reinterpretation of Mamdani Controller 3 fuzzy rules (specified by 3 input-output tuples). The extensional hull is the maximum of all fuzzy rules. R. Kruse, C. Doell FS Fuzzy Control Part 3 56 / 59
60 References I R. Kruse, C. Doell FS Fuzzy Control Part 3 57 / 59
61 References II Jantzen, J. (2013). Foundations of fuzzy control: a practical approach. John Wiley & Sons. Klawonn, F., Gebhardt, J., and Kruse, R. (1995). Fuzzy control on the basis of equality relations with an example from idle speed control. IEEE Transactions on Fuzzy Systems, 3(3): Kruse, R., Borgelt, C., Braune, C., Mostaghim, S., and Steinbrecher, M. (2016). Computational intelligence: a methodological introduction. Springer. Mamdani, E. H. and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1):1 13. Michels, K., Klawonn, F., Kruse, R., and Nürnberger, A. (2006). Fuzzy Control: Fundamentals, Stability and Design of Fuzzy Controllers, volume 200 of Studies in Fuzziness and Soft Computing. Springer, Berlin / Heidelberg, Germany. R. Kruse, C. Doell FS Fuzzy Control Part 3 58 / 59
62 References III Sugeno, M. (1985). An introductory survey of fuzzy control. Information sciences, 36(1-2): R. Kruse, C. Doell FS Fuzzy Control Part 3 59 / 59
Fuzzy 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 informationFuzzy Systems. Theory of Fuzzy Systems
Fuzzy Systems Theory of Fuzzy Systems Prof. Dr. Rudolf Kruse Christoph Doell {kruse,doell}@ovgu.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Institute for Intelligent Cooperating
More informationFuzzy Systems. Introduction
Fuzzy Systems Introduction Prof. Dr. Rudolf Kruse Christian Moewes {kruse,cmoewes}@iws.cs.uni-magdeburg.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Department of Knowledge
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 informationFuzzy Systems. Fuzzy Arithmetic
Fuzzy Systems Fuzzy Arithmetic Prof. Dr. Rudolf Kruse Christian Moewes {kruse,cmoewes}@iws.cs.uni-magdeburg.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Department of Knowledge
More informationModels for Inexact Reasoning. Fuzzy Logic Lesson 8 Fuzzy Controllers. Master in Computational Logic Department of Artificial Intelligence
Models for Inexact Reasoning Fuzzy Logic Lesson 8 Fuzzy Controllers Master in Computational Logic Department of Artificial Intelligence Fuzzy Controllers Fuzzy Controllers are special expert systems KB
More informationAlgorithms for Increasing of the Effectiveness of the Making Decisions by Intelligent Fuzzy Systems
Journal of Electrical Engineering 3 (205) 30-35 doi: 07265/2328-2223/2050005 D DAVID PUBLISHING Algorithms for Increasing of the Effectiveness of the Making Decisions by Intelligent Fuzzy Systems Olga
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 informationME 534. Mechanical Engineering University of Gaziantep. Dr. A. Tolga Bozdana Assistant Professor
ME 534 Intelligent Manufacturing Systems Chp 4 Fuzzy Logic Mechanical Engineering University of Gaziantep Dr. A. Tolga Bozdana Assistant Professor Motivation and Definition Fuzzy Logic was initiated by
More information5. Lecture Fuzzy Systems
Soft Control (AT 3, RMA) 5. Lecture Fuzzy Systems Fuzzy Control 5. Structure of the lecture. Introduction Soft Control: Definition and delimitation, basic of 'intelligent' systems 2. Knowledge representation
More informationFuzzy Control Systems Process of Fuzzy Control
Fuzzy Control Systems The most widespread use of fuzzy logic today is in fuzzy control applications. Across section of applications that have successfully used fuzzy control includes: Environmental Control
More informationFUZZY CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL CONVENTIONAL CONTROL
Eample: design a cruise control system After gaining an intuitive understanding of the plant s dynamics and establishing the design objectives, the control engineer typically solves the cruise control
More informationLearning from Examples
Learning from Examples Adriano Cruz, adriano@nce.ufrj.br PPGI-UFRJ September 20 Adriano Cruz, adriano@nce.ufrj.br (PPGI-UFRJ) Learning from Examples September 20 / 40 Summary Introduction 2 Learning from
More informationOUTLINE. Introduction History and basic concepts. Fuzzy sets and fuzzy logic. Fuzzy clustering. Fuzzy inference. Fuzzy systems. Application examples
OUTLINE Introduction History and basic concepts Fuzzy sets and fuzzy logic Fuzzy clustering Fuzzy inference Fuzzy systems Application examples "So far as the laws of mathematics refer to reality, they
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 informationRamchandraBhosale, Bindu R (Electrical Department, Fr.CRIT,Navi Mumbai,India)
Indirect Vector Control of Induction motor using Fuzzy Logic Controller RamchandraBhosale, Bindu R (Electrical Department, Fr.CRIT,Navi Mumbai,India) ABSTRACT: AC motors are widely used in industries for
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 informationFuzzy Function: Theoretical and Practical Point of View
EUSFLAT-LFA 2011 July 2011 Aix-les-Bains, France Fuzzy Function: Theoretical and Practical Point of View Irina Perfilieva, University of Ostrava, Inst. for Research and Applications of Fuzzy Modeling,
More informationInstitute for Advanced Management Systems Research Department of Information Technologies Åbo Akademi University. Fuzzy Logic Controllers - Tutorial
Institute for Advanced Management Systems Research Department of Information Technologies Åbo Akademi University Directory Table of Contents Begin Article Fuzzy Logic Controllers - Tutorial Robert Fullér
More informationComparison of Fuzzy Operators for IF-Inference Systems of Takagi-Sugeno Type in Ozone Prediction
Comparison of Fuzzy Operators for IF-Inference Systems of Takagi-Sugeno Type in Ozone Prediction Vladimír Olej and Petr Hájek Institute of System Engineering and Informatics, Faculty of Economics and Administration,
More informationRevision: Fuzzy logic
Fuzzy Logic 1 Revision: Fuzzy logic Fuzzy logic can be conceptualized as a generalization of classical logic. Modern fuzzy logic aims to model those problems in which imprecise data must be used or in
More informationRough Approach to Fuzzification and Defuzzification in Probability Theory
Rough Approach to Fuzzification and Defuzzification in Probability Theory G. Cattaneo and D. Ciucci Dipartimento di Informatica, Sistemistica e Comunicazione Università di Milano Bicocca, Via Bicocca degli
More informationFinitely Valued Indistinguishability Operators
Finitely Valued Indistinguishability Operators Gaspar Mayor 1 and Jordi Recasens 2 1 Department of Mathematics and Computer Science, Universitat de les Illes Balears, 07122 Palma de Mallorca, Illes Balears,
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 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 informationFuzzy Logic Notes. Course: Khurshid Ahmad 2010 Typset: Cathal Ormond
Fuzzy Logic Notes Course: Khurshid Ahmad 2010 Typset: Cathal Ormond April 25, 2011 Contents 1 Introduction 2 1.1 Computers......................................... 2 1.2 Problems..........................................
More informationTowards Smooth Monotonicity in Fuzzy Inference System based on Gradual Generalized Modus Ponens
8th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2013) Towards Smooth Monotonicity in Fuzzy Inference System based on Gradual Generalized Modus Ponens Phuc-Nguyen Vo1 Marcin
More informationHandling Uncertainty using FUZZY LOGIC
Handling Uncertainty using FUZZY LOGIC Fuzzy Set Theory Conventional (Boolean) Set Theory: 38 C 40.1 C 41.4 C 38.7 C 39.3 C 37.2 C 42 C Strong Fever 38 C Fuzzy Set Theory: 38.7 C 40.1 C 41.4 C More-or-Less
More informationFUZZY CONTROL OF CHAOS
FUZZY CONTROL OF CHAOS OSCAR CALVO, CICpBA, L.E.I.C.I., Departamento de Electrotecnia, Facultad de Ingeniería, Universidad Nacional de La Plata, 1900 La Plata, Argentina JULYAN H. E. CARTWRIGHT, Departament
More informationFuzzy Logic and Fuzzy Systems
Fuzzy Logic and Fuzzy Systems Revision Lecture Khurshid Ahmad, Professor of Computer Science, Department of Computer Science Trinity College, Dublin-2, IRELAND 24 February 2008. https://www.cs.tcd.ie/khurshid.ahmad/teaching.html
More informationFuzzy control systems. Miklós Gerzson
Fuzzy control systems Miklós Gerzson 2016.11.24. 1 Introduction The notion of fuzziness: type of car the determination is unambiguous speed of car can be measured, but the judgment is not unambiguous:
More information1. Brief History of Intelligent Control Systems Design Technology
Acknowledgments We would like to express our appreciation to Professor S.V. Ulyanov for his continuous help, value corrections and comments to the organization of this paper. We also wish to acknowledge
More informationFuzzy Systems. Possibility Theory
Fuzzy Systems Possibility Theory 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
More informationFUZZY CONTROL OF CHAOS
International Journal of Bifurcation and Chaos, Vol. 8, No. 8 (1998) 1743 1747 c World Scientific Publishing Company FUZZY CONTROL OF CHAOS OSCAR CALVO CICpBA, L.E.I.C.I., Departamento de Electrotecnia,
More informationReduced Size Rule Set Based Fuzzy Logic Dual Input Power System Stabilizer
772 NATIONAL POWER SYSTEMS CONFERENCE, NPSC 2002 Reduced Size Rule Set Based Fuzzy Logic Dual Input Power System Stabilizer Avdhesh Sharma and MLKothari Abstract-- The paper deals with design of fuzzy
More informationFinancial Informatics XI: Fuzzy Rule-based Systems
Financial Informatics XI: Fuzzy Rule-based Systems Khurshid Ahmad, Professor of Computer Science, Department of Computer Science Trinity College, Dublin-2, IRELAND November 19 th, 28. https://www.cs.tcd.ie/khurshid.ahmad/teaching.html
More informationFUZZY LOGIC CONTROL Vs. CONVENTIONAL PID CONTROL OF AN INVERTED PENDULUM ROBOT
http:// FUZZY LOGIC CONTROL Vs. CONVENTIONAL PID CONTROL OF AN INVERTED PENDULUM ROBOT 1 Ms.Mukesh Beniwal, 2 Mr. Davender Kumar 1 M.Tech Student, 2 Asst.Prof, Department of Electronics and Communication
More informationFuzzy Systems. Neuro-Fuzzy Systems
Fuzzy Systems Neuro-Fuzzy Systems Prof. Dr. Rudolf Kruse Christian Moewes {kruse,cmoewes}@iws.cs.uni-magdeburg.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Department of Knowledge
More informationFuzzy Logic Control for Half Car Suspension System Using Matlab
Fuzzy Logic Control for Half Car Suspension System Using Matlab Mirji Sairaj Gururaj 1, Arockia Selvakumar A 2 1,2 School of Mechanical and Building Sciences, VIT Chennai, Tamilnadu, India Abstract- To
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 informationFUZZY LOGIC CONTROL of SRM 1 KIRAN SRIVASTAVA, 2 B.K.SINGH 1 RajKumar Goel Institute of Technology, Ghaziabad 2 B.T.K.I.T.
FUZZY LOGIC CONTROL of SRM 1 KIRAN SRIVASTAVA, 2 B.K.SINGH 1 RajKumar Goel Institute of Technology, Ghaziabad 2 B.T.K.I.T., Dwarhat E-mail: 1 2001.kiran@gmail.com,, 2 bksapkec@yahoo.com ABSTRACT The fuzzy
More informationFuzzy Controller. Fuzzy Inference System. Basic Components of Fuzzy Inference System. Rule based system: Contains a set of fuzzy rules
Fuzz Controller Fuzz Inference Sstem Basic Components of Fuzz Inference Sstem Rule based sstem: Contains a set of fuzz rules Data base dictionar: Defines the membership functions used in the rules base
More informationFuzzy Logic Controller Based on Association Rules
Annals of the University of Craiova, Mathematics and Computer Science Series Volume 37(3), 2010, Pages 12 21 ISSN: 1223-6934 Fuzzy Logic Controller Based on Association Rules Ion IANCU and Mihai GABROVEANU
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 informationHamidreza Rashidy Kanan. Electrical Engineering Department, Bu-Ali Sina University
Lecture 3 Fuzzy Systems and their Properties Hamidreza Rashidy Kanan Assistant Professor, Ph.D. Electrical Engineering Department, Bu-Ali Sina University h.rashidykanan@basu.ac.ir; kanan_hr@yahoo.com 2
More informationFuzzy Logic and Computing with Words. Ning Xiong. School of Innovation, Design, and Engineering Mälardalen University. Motivations
/3/22 Fuzzy Logic and Computing with Words Ning Xiong School of Innovation, Design, and Engineering Mälardalen University Motivations Human centric intelligent systems is a hot trend in current research,
More informationDesign of Decentralized Fuzzy Controllers for Quadruple tank Process
IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.11, November 2008 163 Design of Fuzzy Controllers for Quadruple tank Process R.Suja Mani Malar1 and T.Thyagarajan2, 1 Assistant
More informationFuzzy Sets, Fuzzy Logic, and Fuzzy Systems II
Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems II SSIE 617 Fall 2008 Radim BELOHLAVEK Dept. Systems Sci. & Industrial Eng. Watson School of Eng. and Applied Sci. Binghamton University SUNY Radim Belohlavek
More informationFundamentals. 2.1 Fuzzy logic theory
Fundamentals 2 In this chapter we briefly review the fuzzy logic theory in order to focus the type of fuzzy-rule based systems with which we intend to compute intelligible models. Although all the concepts
More informationEEE 8005 Student Directed Learning (SDL) Industrial Automation Fuzzy Logic
EEE 8005 Student Directed Learning (SDL) Industrial utomation Fuzzy Logic Desire location z 0 Rot ( y, φ ) Nail cos( φ) 0 = sin( φ) 0 0 0 0 sin( φ) 0 cos( φ) 0 0 0 0 z 0 y n (0,a,0) y 0 y 0 z n End effector
More informationML in Practice: CMSC 422 Slides adapted from Prof. CARPUAT and Prof. Roth
ML in Practice: CMSC 422 Slides adapted from Prof. CARPUAT and Prof. Roth N-fold cross validation Instead of a single test-training split: train test Split data into N equal-sized parts Train and test
More informationChapter 2 Introduction to Fuzzy Systems
Chapter 2 Introduction to Fuzzy Systems Robert Czabanski, Michal Jezewski and Jacek Leski Abstract The following chapter describes the basic concepts of fuzzy systems and approximate reasoning. The study
More informationLecture 1: Introduction & Fuzzy Control I
Lecture 1: Introduction & Fuzzy Control I Jens Kober Robert Babuška Knowledge-Based Control Systems (SC42050) Cognitive Robotics 3mE, Delft University of Technology, The Netherlands 12-02-2018 Lecture
More informationFuzzy Systems. Fuzzy Sets and Fuzzy Logic
Fuzzy Systems Fuzzy Sets and Fuzzy Logic Prof. Dr. Rudolf Kruse Christoph Doell {kruse,doell}@ovgu.de Otto-von-Guericke University of Magdeburg Faculty of Computer Science Institute of Intelligent Cooperating
More informationFuzzy Systems for Control Applications
Fuzzy Systems for Control Applications Emil M. Petriu School of Electrical Engineering and Computer Science University of Ottawa http://www.site.uottawa.ca/~petriu/ FUZZY SETS Definition: If X is a collection
More informationApplied Logic. Lecture 3 part 1 - Fuzzy logic. Marcin Szczuka. Institute of Informatics, The University of Warsaw
Applied Logic Lecture 3 part 1 - Fuzzy logic Marcin Szczuka Institute of Informatics, The University of Warsaw Monographic lecture, Spring semester 2017/2018 Marcin Szczuka (MIMUW) Applied Logic 2018 1
More informationEfficient Approximate Reasoning with Positive and Negative Information
Efficient Approximate Reasoning with Positive and Negative Information Chris Cornelis, Martine De Cock, and Etienne Kerre Fuzziness and Uncertainty Modelling Research Unit, Department of Applied Mathematics
More informationRule-Based Fuzzy Model
In rule-based fuzzy systems, the relationships between variables are represented by means of fuzzy if then rules of the following general form: Ifantecedent proposition then consequent proposition The
More informationNeural Networks & Fuzzy Logic
Journal of Computer Applications ISSN: 0974 1925, Volume-5, Issue EICA2012-4, February 10, 2012 Neural Networks & Fuzzy Logic Elakkiya Prabha T Pre-Final B.Tech-IT, M.Kumarasamy College of Engineering,
More informationA Hybrid Approach For Air Conditioning Control System With Fuzzy Logic Controller
International Journal of Engineering and Applied Sciences (IJEAS) A Hybrid Approach For Air Conditioning Control System With Fuzzy Logic Controller K.A. Akpado, P. N. Nwankwo, D.A. Onwuzulike, M.N. Orji
More informationResearch Article P-Fuzzy Diffusion Equation Using Rules Base
Applied Mathematics, Article ID, pages http://dx.doi.org/.// Research Article P-Fuzzy Diffusion Equation Using Rules Base Jefferson Leite, R. C. Bassanezi, Jackellyne Leite, and Moiseis Cecconello Federal
More informationIntuitionistic Fuzzy Logic Control for Washing Machines
Indian Journal of Science and Technology, Vol 7(5), 654 661, May 2014 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 Intuitionistic Fuzzy Logic Control for Washing Machines Muhammad Akram *, Shaista
More informationAn Evaluation of the Reliability of Complex Systems Using Shadowed Sets and Fuzzy Lifetime Data
International Journal of Automation and Computing 2 (2006) 145-150 An Evaluation of the Reliability of Complex Systems Using Shadowed Sets and Fuzzy Lifetime Data Olgierd Hryniewicz Systems Research Institute
More informationUNIVERSITY OF SURREY
UNIVERSITY OF SURREY B.Sc. Undergraduate Programmes in Computing B.Sc. Undergraduate Programmes in Mathematical Studies Level HE3 Examination MODULE CS364 Artificial Intelligence Time allowed: 2 hours
More informationFUZZY CONTROL. Main bibliography
FUZZY CONTROL Main bibliography J.M.C. Sousa and U. Kaymak. Fuzzy Decision Making in Modeling and Control. World Scientific Series in Robotics and Intelligent Systems, vol. 27, Dec. 2002. FakhreddineO.
More informationBandler-Kohout Subproduct with Yager s classes of Fuzzy Implications
Bandler-Kohout Subproduct with Yager s classes of Fuzzy Implications Sayantan Mandal and Balasubramaniam Jayaram, Member, IEEE Abstract The Bandler Kohout Subproduct BKS inference mechanism is one of the
More informationDesign On-Line Tunable Gain Artificial Nonlinear Controller
Journal of Computer Engineering 1 (2009) 3-11 Design On-Line Tunable Gain Artificial Nonlinear Controller Farzin Piltan, Nasri Sulaiman, M. H. Marhaban and R. Ramli Department of Electrical and Electronic
More informationHybrid Logic and Uncertain Logic
Journal of Uncertain Systems Vol.3, No.2, pp.83-94, 2009 Online at: www.jus.org.uk Hybrid Logic and Uncertain Logic Xiang Li, Baoding Liu Department of Mathematical Sciences, Tsinghua University, Beijing,
More informationFitting Aggregation Functions to Data: Part II Idempotization
Fitting Aggregation Functions to Data: Part II Idempotization Maciej Bartoszuk 1, Gleb Beliakov 2, Marek Gagolewski 3,1, and Simon James 2 1 Faculty of Mathematics and Information Science, Warsaw University
More informationUncertain System Control: An Engineering Approach
Uncertain System Control: An Engineering Approach Stanisław H. Żak School of Electrical and Computer Engineering ECE 680 Fall 207 Fuzzy Logic Control---Another Tool in Our Control Toolbox to Cope with
More informationUncertain Systems are Universal Approximators
Uncertain Systems are Universal Approximators Zixiong Peng 1 and Xiaowei Chen 2 1 School of Applied Mathematics, Central University of Finance and Economics, Beijing 100081, China 2 epartment of Risk Management
More informationFuzzy expert systems
The Islamic University of Gaza Faculty of Engineering Dept. of Computer Engineering ECOM5039:Artificial Intelligence Eng. Ibraheem Lubbad Fuzzy expert systems Main points: Fuzzy logic is determined as
More informationNeural Networks. Prof. Dr. Rudolf Kruse. Computational Intelligence Group Faculty for Computer Science
Neural Networks Prof. Dr. Rudolf Kruse Computational Intelligence Group Faculty for Computer Science kruse@iws.cs.uni-magdeburg.de Rudolf Kruse Neural Networks 1 Supervised Learning / Support Vector Machines
More informationFuzzy logic : principles and applications
École d été Franco Roumaine Commande Avancée des Systèmes & Nouvelles Technologies Informatiques CA NTI 2015 Fuzzy logic : principles and applications Dr. Ing. Professor-Researcher Co-responsable of ESEA
More informationEffect of Rule Weights in Fuzzy Rule-Based Classification Systems
506 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 9, NO. 4, AUGUST 2001 Effect of Rule Weights in Fuzzy Rule-Based Classification Systems Hisao Ishibuchi, Member, IEEE, and Tomoharu Nakashima, Member, IEEE
More informationFuzzy Logic. An introduction. Universitat Politécnica de Catalunya. Departament de Teoria del Senyal i Comunicacions.
Universitat Politécnica de Catalunya Departament de Teoria del Senyal i Comunicacions Fuzzy Logic An introduction Prepared by Temko Andrey 2 Outline History and sphere of applications Basics. Fuzzy sets
More informationDesign of the Models of Neural Networks and the Takagi-Sugeno Fuzzy Inference System for Prediction of the Gross Domestic Product Development
Design of the Models of Neural Networks and the Takagi-Sugeno Fuzzy Inference System for Prediction of the Gross Domestic Product Development VLADIMÍR OLEJ Institute of System Engineering and Informatics
More informationActa Mathematica Academiae Paedagogicae Nyíregyháziensis 18 (2002),
Acta Mathematica Academiae Paedagogicae Nyíregyháziensis 18 (2002), 77 84 www.emis.de/journals STRUCTURE REPRESENTATION IN OBJECT ORIENTED KNOWLEDGE REPRESENTATION SYSTEMS KATALIN BOGNÁR Abstract. This
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 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 informationCS 354R: Computer Game Technology
CS 354R: Computer Game Technology AI Fuzzy Logic and Neural Nets Fall 2017 Fuzzy Logic Philosophical approach Decisions based on degree of truth Is not a method for reasoning under uncertainty that s probability
More informationSimilarity-based Classification with Dominance-based Decision Rules
Similarity-based Classification with Dominance-based Decision Rules Marcin Szeląg, Salvatore Greco 2,3, Roman Słowiński,4 Institute of Computing Science, Poznań University of Technology, 60-965 Poznań,
More informationABSTRACT I. INTRODUCTION II. FUZZY MODEL SRUCTURE
International Journal of Scientific Research in Computer Science, Engineering and Information Technology 2018 IJSRCSEIT Volume 3 Issue 6 ISSN : 2456-3307 Temperature Sensitive Short Term Load Forecasting:
More informationML (cont.): SUPPORT VECTOR MACHINES
ML (cont.): SUPPORT VECTOR MACHINES CS540 Bryan R Gibson University of Wisconsin-Madison Slides adapted from those used by Prof. Jerry Zhu, CS540-1 1 / 40 Support Vector Machines (SVMs) The No-Math Version
More informationFuzzy Reasoning with a Rete-OO Rule Engine
Fuzzy Reasoning with a Rete-OO Rule Engine University of Applied Sciences Münster, Germany 1 DEIS, Facolta di Ingegneria, Universita di Bologna, Italy 2 Nikolaus Wulff 1 (Speaker) Davide Sottara 2 Why
More informationWhere are we? Operations on fuzzy sets (cont.) Fuzzy Logic. Motivation. Crisp and fuzzy sets. Examples
Operations on fuzzy sets (cont.) G. J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, chapters -5 Where are we? Motivation Crisp and fuzzy sets alpha-cuts, support,
More informationLecture 06. (Fuzzy Inference System)
Lecture 06 Fuzzy Rule-based System (Fuzzy Inference System) Fuzzy Inference System vfuzzy inference is the process of formulating the mapping from a given input to an output using fuzzy logic. Fuzzy Inference
More informationFuzzy Control of a Multivariable Nonlinear Process
Fuzzy Control of a Multivariable Nonlinear Process A. Iriarte Lanas 1, G. L.A. Mota 1, R. Tanscheit 1, M.M. Vellasco 1, J.M.Barreto 2 1 DEE-PUC-Rio, CP 38.063, 22452-970 Rio de Janeiro - RJ, Brazil e-mail:
More informationNEURO-FUZZY SYSTEM BASED ON GENETIC ALGORITHM FOR ISOTHERMAL CVI PROCESS FOR CARBON/CARBON COMPOSITES
NEURO-FUZZY SYSTEM BASED ON GENETIC ALGORITHM FOR ISOTHERMAL CVI PROCESS FOR CARBON/CARBON COMPOSITES Zhengbin Gu, Hejun Li, Hui Xue, Aijun Li, Kezhi Li College of Materials Science and Engineering, Northwestern
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 informationPerformance Of Power System Stabilizerusing Fuzzy Logic Controller
IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-issn: 2278-1676,p-ISSN: 2320-3331, Volume 9, Issue 3 Ver. I (May Jun. 2014), PP 42-49 Performance Of Power System Stabilizerusing Fuzzy
More informationCircuit Implementation of a Variable Universe Adaptive Fuzzy Logic Controller. Weiwei Shan
Circuit Implementation of a Variable Universe Adaptive Fuzzy Logic Controller Weiwei Shan Outline 1. Introduction: Fuzzy logic and Fuzzy control 2. Basic Ideas of Variable Universe of Discourse 3. Algorithm
More informationA Study on Performance of Fuzzy And Fuzyy Model Reference Learning Pss In Presence of Interaction Between Lfc and avr Loops
Australian Journal of Basic and Applied Sciences, 5(2): 258-263, 20 ISSN 99-878 A Study on Performance of Fuzzy And Fuzyy Model Reference Learning Pss In Presence of Interaction Between Lfc and avr Loops
More informationOn Perception-based Logical Deduction and Its Variants
16th World Congress of the International Fuzzy Systems Association (IFSA) 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT) On Perception-based Logical Deduction and Its Variants
More informationLyapunov Function Based Design of Heuristic Fuzzy Logic Controllers
Lyapunov Function Based Design of Heuristic Fuzzy Logic Controllers L. K. Wong F. H. F. Leung P. IS.S. Tam Department of Electronic Engineering Department of Electronic Engineering Department of Electronic
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 informationLOW COST FUZZY CONTROLLERS FOR CLASSES OF SECOND-ORDER SYSTEMS. Stefan Preitl, Zsuzsa Preitl and Radu-Emil Precup
Copyright 2002 IFAC 15th Triennial World Congress, Barcelona, Spain LOW COST FUZZY CONTROLLERS FOR CLASSES OF SECOND-ORDER SYSTEMS Stefan Preitl, Zsuzsa Preitl and Radu-Emil Precup Politehnica University
More informationDirect Torque Control of Three Phase Induction Motor Using Fuzzy Logic
Direct Torque Control of Three Phase Induction Motor Using Fuzzy Logic Mr. Rajendra S. Soni 1, Prof. S. S. Dhamal 2 1 Student, M. E. Electrical (Control Systems), K. K. Wagh College of Engg.& Research,
More informationFuzzy Gain Scheduling and Tuning of Multivariable Fuzzy Control Methods of Fuzzy Computing in Control Systems
Tampereen teknillinen korkeakoulu Julkaisuja 293 Tampere University of Technology Publications 293 Pauli Viljamaa Fuzzy Gain Scheduling and Tuning of Multivariable Fuzzy Control Methods of Fuzzy Computing
More informationInverted Fuzzy Implications in Backward Reasoning Without Yager Implication
Inverted Fuy Implications in Backward Reasoning Without Yager Implication Zbigniew Suraj 1 and Agnieska Lasek 1 Chair of Computer Science, Faculty of Mathematics and Natural Sciences, University of Resow,
More information