Abstract
A systematic identification method that realizes fuzzy modeling using the input and output data pairs of system is presented. Such a model is composed of fuzzy implications. The implications are automatically generated by the structure and parameter identification. In the structure identification the optimal or near optimal number of fuzzy implications is determined in view of valid partition of data set. The parameters defining the fuzzy implications re identified by a GA (Genetic Algorithm) hybrid scheme to minimize mean square errors globally. Numerical examples are provided to evaluate the feasibility of the proposed approach. Comparison shows that the suggested approach can produce a fuzzy model with higher accuracy and a smaller number of fuzzy implications than the ones achieved previously in other methods. The proposed approach has also been applied to construct a fuzzy model for the navigation control of a mobile robot. The validity of the resultant model is demonstrated by experimentation.
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© 1995 Kluwer Academic Publishers
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Joo, Y.H., Hwang, H.S., Woo, K.B., Kim, K.B. (1995). Fuzzy System Modeling and its Application to Mobile Robot Control. In: Bien, Z., Min, K.C. (eds) Fuzzy Logic and its Applications to Engineering, Information Sciences, and Intelligent Systems. Theory and Decision Library, vol 16. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0125-4_14
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DOI: https://doi.org/10.1007/978-94-009-0125-4_14
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-6543-6
Online ISBN: 978-94-009-0125-4
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