DocumentCode :
759395
Title :
Evolving Compact and Interpretable Takagi–Sugeno Fuzzy Models With a New Encoding Scheme
Author :
Kim, Min-Soeng ; Kim, Chang-Hyun ; Lee, Ju-Jang
Author_Institution :
Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Daejon
Volume :
36
Issue :
5
fYear :
2006
Firstpage :
1006
Lastpage :
1023
Abstract :
Developing Takagi-Sugeno fuzzy models by evolutionary algorithms mainly requires three factors: an encoding scheme, an evaluation method, and appropriate evolutionary operations. At the same time, these three factors should be designed so that they can consider three important aspects of fuzzy modeling: modeling accuracy, compactness, and interpretability. This paper proposes a new evolutionary algorithm that fulfills such requirements and solves fuzzy modeling problems. Two major ideas proposed in this paper lie in a new encoding scheme and a new fitness function, respectively. The proposed encoding scheme consists of three chromosomes, one of which uses unique chained possibilistic representation of rule structure. The proposed encoding scheme can achieve simultaneous optimization of parameters of antecedent membership functions and rule structures with the new fitness function developed in this paper. The proposed fitness function consists of five functions that consider three evaluation criteria in fuzzy modeling problems. The proposed fitness function guides evolutionary search direction so that the proposed algorithm can find more accurate compact fuzzy models with interpretable antecedent membership functions. Several evolutionary operators that are appropriate for the proposed encoding scheme are carefully designed. Simulation results on three modeling problems show that the proposed encoding scheme and the proposed fitness functions are effective in finding accurate, compact, and interpretable Takagi-Sugeno fuzzy models. From the simulation results, it is shown that the proposed algorithm can successfully find fuzzy models that approximate the given unknown function accurately with a compact number of fuzzy rules and membership functions. At the same time, the fuzzy models use interpretable antecedent membership functions, which are helpful in understanding the underlying behavior of the obtained fuzzy models
Keywords :
encoding; evolutionary computation; fuzzy set theory; fuzzy systems; mathematical operators; optimisation; possibility theory; search problems; Takagi-Sugeno fuzzy modeling; encoding scheme; evolutionary operators; evolutionary search algorithm; fitness function; fuzzy rule structure; interpretable antecedent membership function; unique chained possibilistic representation; Biological cells; Design methodology; Encoding; Evolutionary computation; Fuzzy neural networks; Information technology; Least squares approximation; Multidimensional systems; Neural networks; Robustness; Evolutionary algorithm (EA); Takagi–Sugeno (TS) fuzzy model; fuzzy modeling; interpretability;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2006.872265
Filename :
1703645
Link To Document :
بازگشت