IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
System identification using hierarchical fuzzy neural networks with stable learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
IEEE Transactions on Fuzzy Systems
Intermediate variable normalization for gradient descent learning for hierarchical fuzzy system
IEEE Transactions on Fuzzy Systems
Fuzzy Sets and Systems
Rule base identification in fuzzy networks by Boolean matrix equations
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Complexity management methodology for fuzzy systems with feedback rule bases
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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This paper presents a class of hierarchical fuzzy systems 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 continuous 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 approximates 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.