Comparison of necessary conditions for typical Takagi-Sugeno and Mamdani fuzzy systems as universal approximators

• Published in: IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 29; no. 5; pp. 508 - 514
• Main Authors: Ying, Hao, Ding, Yongsheng, Li, Shaokuan, Shao, Shihuang
• Format: Journal Article
• Language: English
• Published: IEEE 01-09-1999
• Subjects: Approximation; Biomedical engineering; Control system synthesis; Cybernetics; Fuzzy; Fuzzy control; Fuzzy logic; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Input variables; Mathematical analysis; Mathematical models; Nonlinear control systems; Takagi-Sugeno model; Textiles
• ISSN: 1083-4427; 1558-2426
• DOI: 10.1109/3468.784177

Both Takagi-Sugeno (TS) and Mamdani fuzzy systems are known to be universal approximators. We investigate whether one type of fuzzy approximators is more economical than the other. The TS fuzzy systems are the typical two-input single-output TS fuzzy systems. We first establish necessary conditions on minimal system configuration of the TS fuzzy systems as function approximators. We show that the number of the input fuzzy sets and fuzzy rules needed by the TS fuzzy systems depend on the number and locations of the extrema of the function to be approximated. The resulting conditions reveal the strength of the TS fuzzy approximators. The drawback, though, is that a large number of fuzzy rules must be employed to approximate periodic or highly oscillatory functions. We then compare these necessary conditions with the ones that we established for the general Mamdani fuzzy systems in our previous papers. Results of the comparison unveil that the minimal system configurations of the TS and Mamdani fuzzy systems are comparable. Finally, we prove that the minimal configuration of the TS fuzzy systems can be reduced and becomes smaller than that of the Mamdani fuzzy systems if nontrapezoidal or nontriangular input fuzzy sets are used. We believe that all the results in present paper hold for the TS fuzzy systems with more than two input variables but the proof seems to be mathematically difficult. Our new findings are valuable in designing more compact fuzzy systems, especially fuzzy controllers and models which are two most popular and successful applications of the fuzzy approximators.