A class of hierarchical fuzzy systems with constraints on the fuzzy rules.pdf

A class of hierarchical fuzzy systems with constraints on the fuzzy rules.pdf

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A class of hierarchical fuzzy systems with constraints on the fuzzy rules

194 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 13, NO. 2, APRIL 2005 A Class of Hierarchical Fuzzy Systems With Constraints on the Fuzzy Rules Moon G. Joo and Jin S. Lee Abstract—This paper presents a class of hierarchical fuzzy sys- tems where previous layer outputs are used not in IF-parts, but only in THEN-parts of the fuzzy rules of the current layer. The proposed scheme is shown to be a universal approximator to any real contin- uous function on a compact set if complete fuzzy sets are used in the IF-parts of the fuzzy rules with singleton fuzzifier and center average defuzzifier. From the example of ball-and-beam control system simulation, it is demonstrated that the proposed scheme ap- proximates with high accuracy a model nonlinear controller with fewer fuzzy rules than a centralized fuzzy system, and its control performance is comparable to that of a nonlinear controller. Index Terms—Fuzzy control, hierarchical fuzzy logic system, Stone–Weierstrass theorem, universal approximation. I. INTRODUCTION ONE OF THE important issues in fuzzy logic systems ishow to reduce the number of involved fuzzy rules and their corresponding computation requirements. In fact, the number of fuzzy rules grows exponentially with the number of input variables. Specifically, a single-output fuzzy logic system with input variables and fuzzy sets defined for each input variable requires number of fuzzy rules. To overcome the problem, the idea of using hierarchical structure in designing a fuzzy system has been reported by Raju and Zhou [1], [2], where input variables are put into a collection of low-dimensional fuzzy logic units (FLUs) and the outputs of the FLUs are used as input variables for the FLU in the next layer. According to them, the number of fuzzy rules employed in a hierarchical fuzzy system (HFS) is proportional to the number of input variables. In HFS, however, the retrieval of physical meanings from the outputs of the FLUs in the previous layer with or without sup- pleme

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