Power law and dimension of the maximum value for belief distribution with the max Deng entropy

Similar documents
A new belief entropy: possible generalization of Deng entropy, Tsallis entropy and Shannon entropy

International Journal of Mathematical Archive-3(3), 2012, Page: Available online through ISSN

An Improved multiple fractal algorithm

The Order Relation and Trace Inequalities for. Hermitian Operators

A New Scrambling Evaluation Scheme based on Spatial Distribution Entropy and Centroid Difference of Bit-plane

A New Evidence Combination Method based on Consistent Strength

Bayesian predictive Configural Frequency Analysis

A New Evolutionary Computation Based Approach for Learning Bayesian Network

CHAPTER-5 INFORMATION MEASURE OF FUZZY MATRIX AND FUZZY BINARY RELATION

The Two-scale Finite Element Errors Analysis for One Class of Thermoelastic Problem in Periodic Composites

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,

Fuzzy Boundaries of Sample Selection Model

High resolution entropy stable scheme for shallow water equations

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM

Formalisms For Fusion Belief in Design

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

Complement of Type-2 Fuzzy Shortest Path Using Possibility Measure

The binomial transforms of the generalized (s, t )-Jacobsthal matrix sequence

Linear Approximation with Regularization and Moving Least Squares

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Bayesian Networks. Course: CS40022 Instructor: Dr. Pallab Dasgupta

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

The lower and upper bounds on Perron root of nonnegative irreducible matrices

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Quantum and Classical Information Theory with Disentropy

829. An adaptive method for inertia force identification in cantilever under moving mass

On an Extension of Stochastic Approximation EM Algorithm for Incomplete Data Problems. Vahid Tadayon 1

In this section is given an overview of the common elasticity models.

The Study of Teaching-learning-based Optimization Algorithm

CONTRAST ENHANCEMENT FOR MIMIMUM MEAN BRIGHTNESS ERROR FROM HISTOGRAM PARTITIONING INTRODUCTION

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Canonical transformations

Thermodynamics and statistical mechanics in materials modelling II

ECE559VV Project Report

An Application of Fuzzy Hypotheses Testing in Radar Detection

Orientation Model of Elite Education and Mass Education

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body

Inductance Calculation for Conductors of Arbitrary Shape

2E Pattern Recognition Solutions to Introduction to Pattern Recognition, Chapter 2: Bayesian pattern classification

A Hybrid Variational Iteration Method for Blasius Equation

Adaptive Consensus Control of Multi-Agent Systems with Large Uncertainty and Time Delays *

An (almost) unbiased estimator for the S-Gini index

Comparative Studies of Law of Conservation of Energy. and Law Clusters of Conservation of Generalized Energy

arxiv: v1 [math.co] 12 Sep 2014

Soft Neutrosophic Bi-LA-semigroup and Soft Neutrosophic N-LA-seigroup

Case Study of Markov Chains Ray-Knight Compactification

A note on almost sure behavior of randomly weighted sums of φ-mixing random variables with φ-mixing weights

AP Physics 1 & 2 Summer Assignment

Representation theory and quantum mechanics tutorial Representation theory and quantum conservation laws

The Quadratic Trigonometric Bézier Curve with Single Shape Parameter

Chapter 11: Simple Linear Regression and Correlation

The Second Anti-Mathima on Game Theory

Chapter 2 A Class of Robust Solution for Linear Bilevel Programming

Mathematical Preparations

Army Ants Tunneling for Classical Simulations

COMPOSITE BEAM WITH WEAK SHEAR CONNECTION SUBJECTED TO THERMAL LOAD

Research Article Relative Smooth Topological Spaces

Grid Generation around a Cylinder by Complex Potential Functions

A Fast Computer Aided Design Method for Filters

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t

The Symmetries of Kibble s Gauge Theory of Gravitational Field, Conservation Laws of Energy-Momentum Tensor Density and the

The Jacobsthal and Jacobsthal-Lucas Numbers via Square Roots of Matrices

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

A P PL I CA TIONS OF FRACTIONAL EXTERIOR DI F F ER EN TIAL IN THR EE- DI M ENSIONAL S PAC E Ξ

Solution Thermodynamics

Semi-supervised Classification with Active Query Selection

Perfect Competition and the Nash Bargaining Solution

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Valuated Binary Tree: A New Approach in Study of Integers

Goodness of fit and Wilks theorem

Errors in Nobel Prize for Physics (7) Improper Schrodinger Equation and Dirac Equation

An identification algorithm of model kinetic parameters of the interfacial layer growth in fiber composites

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

} Often, when learning, we deal with uncertainty:

Refined Coding Bounds for Network Error Correction

A General Method for Assessing the Uncertainty in Classified Remotely Sensed Data at Pixel Scale

Several generation methods of multinomial distributed random number Tian Lei 1, a,linxihe 1,b,Zhigang Zhang 1,c

On the symmetric character of the thermal conductivity tensor

Global Sensitivity. Tuesday 20 th February, 2018

DESIGN OPTIMIZATION OF CFRP RECTANGULAR BOX SUBJECTED TO ARBITRARY LOADINGS

Estimation: Part 2. Chapter GREG estimation

Numerical Heat and Mass Transfer

Games of Threats. Elon Kohlberg Abraham Neyman. Working Paper

A Network Intrusion Detection Method Based on Improved K-means Algorithm

Supplemental document

P R. Lecture 4. Theory and Applications of Pattern Recognition. Dept. of Electrical and Computer Engineering /

A CHARACTERIZATION OF ADDITIVE DERIVATIONS ON VON NEUMANN ALGEBRAS

NUMERICAL DIFFERENTIATION

An efficient algorithm for multivariate Maclaurin Newton transformation

Assessment of Site Amplification Effect from Input Energy Spectra of Strong Ground Motion

Reasoning under Uncertainty

Double Layered Fuzzy Planar Graph

Application research on rough set -neural network in the fault diagnosis system of ball mill

Uncertainty and auto-correlation in. Measurement

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)

Neutrosophic Bi-LA-Semigroup and Neutrosophic N-LA- Semigroup

Markov Chain Monte Carlo Lecture 6

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications

Pulse Coded Modulation

THEOREMS OF QUANTUM MECHANICS

Transcription:

Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng entropy s a novel and effcent uncertanty measure to deal wth mprecse phenomenon, whch s an extenson of Shannon entropy. In ths paper, power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy are presented, whch partally uncover the nherent physcal meanngs of Deng entropy from the perspectve of statstcs. Ths ndcated some work related to power law or scale-free can be analyzed usng Deng entropy. The results of some numercal smulatons are used to support the new vews. Keywords: Deng entropy, Power law, Maxmum Deng entropy, Dmenson 1. Introducton Deng entropy has been proposed by Prof. Deng to manage the uncertan nformaton n the frame of Dempster-Shafer evdence theory (DST)[1], whch has acheved plenty of attenton recent years[2, 3, 4, 5, 6]. Deng entropy can be consdered as an extenson of Shannon entropy to deal wth Correspondng author: Bngy Kang, College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Emal address: bngy.kang@nwsuaf.edu.cn; bngy.kang@hotmal.com. Preprnt submtted to December 17, 2018

uncertan phenomenon n the probablty feld. In addton, Deng entropy can be appled to absorb the complex mprecse (or unknown) phenomenon n the belef fled (frame of DST) effcently. In ths paper, the work focuses two nvestgatons based on the maxmum values of the belef dstrbuton va the max Deng entropy wth dfferent scales of frame of dscernment (FOD). One s the relaton between the maxmum value of belef dstrbuton subjectng to the max Deng entropy and the scale of Deng nformaton correspondngly. The other s dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy. Some numercal smulatons have been made to acheve the two dscoverngs,.e., approxmate power law and approxmate constant dmenson. The paper s organzed as follows. The prelmnares brefly ntroduce some concepts about Dempster-Shafer evdence theory, Deng entropy and max Deng entropy n Secton 2. In Secton 3, the new vews about max Deng entropy are presented. One s the relaton between the maxmum value of belef dstrbuton subjectng to the max Deng entropy and the scale of Deng nformaton correspondngly. The other s dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy. Fnally, ths paper s concluded n Secton 4. 2. Prelmnares In ths secton, some prelmnares are brefly ntroduced. 2

2.1. Frame of Dempster-Shafer evdence theory Let X be a set of mutually exclusve and collectvely exhaustve events, ndcated by X = {θ 1, θ 2,, θ,, θ X } (1) where set X s called a frame of dscernment (FOD). The power set of X s ndcated by 2 X, namely 2 X = {, {θ 1 },, {θ X }, {θ 1, θ 2 },, {θ 1, θ 2,, θ },, X} (2) For a frame of dscernment X = {θ 1, θ 2,, θ X }, a mass functon s a mappng m from 2 X to [0, 1], formally defned by: m : 2 X [0, 1] (3) whch satsfes the followng condton: m( ) = 0 and A 2 X m(a) = 1 (4) where A s a focal element f m(a) s not 0. 2.2. Deng entropy Wth the range of uncertanty mentoned above, Deng entropy [1] can be presented as follows E d = m(f ) log m(f ) 2 F 1 (5) 3

where, F s a proposton n mass functon m, and F s the cardnalty of F. As shown n the above defnton, Deng entropy, formally, s smlar wth the classcal Shannon entropy, but the belef for each proposton F s dvded by a term (2 F 1) whch represents the potental number of states n F (of course, the empty set s not ncluded). Specally, Deng entropy can defntely degenerate to the Shannon entropy f the belef s only assgned to sngle elements. Namely, E d = m(θ ) log m(θ ) 2 θ 1 = m(θ ) log m(θ ) Next, the condton of the maxmum Deng entropy s dscussed[7]. 2.3. The maxmum Deng entropy Assume F s the focal element and m(f ) s the basc probablty assgnment for F, then the maxmum Deng entropy for a belef functon happens when the basc probablty assgnment satsfy the condton m (F ) = 2 F 1 2 F 1, where = 1, 2,..., 2 X 1, and X s the scale of the frame of dscernment. Theorem 1 (The maxmum Deng entropy). The maxmum Deng entropy: E d = m(f ) log m(f ) f and only f m (F 2 F 1 ) = 2 F 1 2 F 1 More nformaton refers to Appendx A. As shown n Fg. 1, belef dstrbutons wth the maxmum Deng entropy are changng wth the scale of FOD, X =1,..8. The pont n ths paper les n the maxmum value of each belef dstrbuton. 4

Belef dstrbuton changng wth scale of FOD,va Max Deng Entropy 1 Belef Dstrbuton (X=1) 0.9 0.8 0.7 Mass functon 0.6 0.5 0.4 Belef Dstrbuton (X=2) Belef Dstrbuton (X=3) 0.3 0.2 Belef Dstrbuton (X=4) Belef Dstrbuton (X=5) 0.1 Belef Dstrbuton (X=6) Belef Dstrbuton (X=7) Belef Dstrbuton (X=8) 0 0 50 100 150 200 250 300 Fgure 1: Belef dstrbuton wth the maxmum Deng entropy changng wth the scale of FOD, X =1,..8. 5

Next, Two focuses are presented. One s the relaton between the maxmum value of belef dstrbuton subjectng to the max Deng entropy and the scale of Deng nformaton correspondngly. The other s dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy. 3. Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy In statstc, a power law s a relatonshp n whch a relatve change n one quantty gves rse to a proportonal relatve change n the other quantty, ndependent of the ntal sze of those quanttes. Power law s a pervasve phenomenon n many felds, such as complex network (scale-free network) [8], Fractals[9]. Frstly, the power law functon s establshed between the maxmum value for belef dstrbuton va max Deng entropy and the maxmum Deng nformaton scale correspondngly. 3.1. Power law of the maxmum value for belef dstrbuton wth the max Deng entropy Suppose the maxmum value for belef dstrbuton va max Deng entropy max [m (F )] relates to a functon P (r). In addton, assume the correspondng maxmum Deng nformaton scale ( 2 F 1 ) relates to the varable r. ( ( A pow law functon P 2 F 1 )) wth a scale nvarance (d 0.59) s establshed usng Eq. (6). P (r) = r d (6) 6

where r = ( 2 F 1 ), P (r) = max [m (F )], m (F ) = 2 F 1 ),d 0.59. (2 F 1 As shown n Fg. 2, when the scales of FOD ( X ) change from 1 to 10, all the few hgh belef (the maxmum values of the belef dstrbuton va max Deng entropy, max [m (F )]) are contaned n the front of the plane, most of the other low belef (the maxmum values of the belef dstrbuton va max Deng entropy, max [m (F )]) are dstrbuted n the followng wde plane. ( Ths s an approxmate power law dstrbuton. A pow law functon ( P 2 F 1 )) wth a scale nvarance (d) s easly observed by Fg. 2. What s more, the scale nvarance d 0.59, whch wll be dscussed n the next secton. 1 Max value of belef dstrbuton changng wth scale of FOD,va Max Deng Entropy 0.9 0.8 max value of Mass functon 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 200 400 600 800 1000 1200 Sum(2 F -1), X =1,...,10 Fgure 2: The maxmum value of belef dstrbuton wth the maxmum Deng entropy changng wth the maxmum scale of Deng nformaton, the scale of FOD, X =1,..10. Ths s an approxmate power law dstrbuton. 7

Next, dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy s presented. 3.2. Dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy The scale nvarance d n Eq.(6) s equal to the dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy. By the polyf t functon n the Matlab, the dmenson d 0.59 after nvestgatng the data of belef dstrbutons wth max Deng entropy (scale of FOD, X = 1, 2,..., 25). The result s shown n Fg. 3, whch ndcates the log values trendng between the maxmum value of belef dstrbuton wth the maxmum Deng entropy and the maxmum amount of Deng nformaton, the scale of FOD, X =1,..25. As shown n Fg. 3, an approxmate lnear relaton s obtaned, whch ndcate the scale-free and power law. d = lm ε 0 log N (ε) log (ε) s.t. m (F ) = 2 F 1 (2 F 1) log = lm 2 max [m (F )] log 2 (2 F 1) 0.59 (7) (8) 4. Concluson In ths paper, power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy are presented, whch partally uncover the nherent physcal meanngs of Deng entropy from the perspectve of statstcs. The results of some numercal smulatons are used to support 8

0 Trendng between Log2 of max value of Mass functon and Log2 Sum(2 F -1) Log2 of max value of Mass functon -5-10 d 0.59-15 0 5 10 15 20 25 Log2 Sum(2 F -1) Fgure 3: Dmenson of the maxmum value of belef dstrbuton wth the maxmum Deng entropy and the maxmum amount of Deng nformaton, the scale of FOD, X =1,..25. 9

the new vews. Ths ndcated some work related to power law or scale-free can be analyzed usng Deng entropy. Acknowledgments The author greatly apprecates Professor Yong Deng for hs encouragement to do ths research. The work s partally supported by Chna Scholarshp Councl, and ntal Doctoral Natural Scence Foundaton of Northwest A&F Unversty (Grant No. Z109021812). Appendx A. The maxmum Deng entropy Assume F s the focal element and m(f ) s the basc probablty assgnment for F, then the maxmum Deng entropy for a belef functon happens when the basc probablty assgnment satsfy the condton m (F ) = 2 F 1 2 F 1, where = 1, 2,..., 2 X 1, and X s the scale of the frame of dscernment. Theorem 2 (The maxmum Deng entropy). The maxmum Deng entropy: E d = Proof. Let m(f ) log m(f ) f and only f m (F 2 F 1 ) = 2 F 1 D = m (F ) log m (F ) 2 F 1 2 F 1 (A.1) m (F ) = 1 (A.2) 10

Then the Lagrange functon can be defned as D 0 = ( ) m (F ) log m (F ) 2 F 1 + λ m (F ) 1 (A.3) Now we can calculate the gradent, D 0 m (F ) = log m (F ) 2 F 1 m (F ) Then Eq. (A.4) can be smplfed as 1 m(f ) ln a 2 F 1 1 2 F 1 + λ = 0 (A.4) log m (F ) 2 F 1 1 ln a + λ = 0 (A.5) From Eq. (A.5), we can get m (F 1 ) 2 F 1 1 = m (F 2) 2 F 2 1 = = m (F n) 2 Fn 1 (A.6) Let m (F 1 ) 2 F 1 1 = m (F 2) 2 F 2 1 = = m (F n) 2 Fn 1 = k (A.7) Then m (F ) = k ( 2 F 1 ) (A.8) Accordng to Eq. (A.2), we can get k = 1 2 F 1 (A.9) 11

Accordng to Eq. (A.7), we can get m (F ) = 2 F 1 2 F 1 (A.10) Hence, the maxmum Deng entropy E d = m (F ) = 2 F 1 2 F 1 m(f ) log m(f ) 2 F 1 f and only f References [1] Y. Deng, Deng entropy, Chaos, Soltons & Fractals 91 (2016) 549 553. [2] J. Abellán, Analyzng propertes of deng entropy n the theory of evdence, Chaos, Soltons & Fractals 95 (2017) 195 199. [3] R. Jroušek, P. P. Shenoy, A new defnton of entropy of belef functons n the dempster shafer theory, Internatonal Journal of Approxmate Reasonng 92 (2018) 49 65. [4] H. Zheng, Y. Deng, Evaluaton method based on fuzzy relatons between dempster shafer belef structure, Internatonal Journal of Intellgent Systems 33 (7) (2018) 1343 1363. [5] Y. L, Y. Deng, Generalzed ordered propostons fuson based on belef entropy., Internatonal Journal of Computers, Communcatons & Control 13 (5) (2018) 792 807. [6] W. Jang, B. We, C. Xe, D. Zhou, An evdental sensor fuson method 12

n fault dagnoss, Advances n Mechancal Engneerng 8 (3) (2016) 1687814016641820. [7] B. Kang, Y. Deng, The maxmum deng entropy, vxra (2015) 1509.0119. URL http://vxra.org/abs/1509.0119 [8] C. Song, S. Havln, H. A. Makse, Self-smlarty of complex networks, Nature 433 (7024) (2005) 392. [9] D. Avnr, O. Bham, D. Ldar, O. Malca, Is the geometry of nature fractal?, Scence 279 (5347) (1998) 39 40. 13