Abstract
Nonlinear complexities and unknown uncertainty of models for dynamical systems are difficult problems in identification tasks. Fuzzy systems, especially Takagi-Sugeno (TS) fuzzy systems, viewed as nonlinear systems are potential candidates for identification and control of general nonlinear systems. A method of nonlinear identification in open-loop based on TS fuzzy system is evaluated in this paper. The contribution of this paper is the proposition of an optimization approach to automatically build a TS fuzzy system based on a set of input-output data of a process. The proposed scheme is based in particle swarm optimization with operators based on Gaussian and Cauchy distributions for the antecedent part design, while least mean squares technique is utilized for consequent part of production rules of a TS fuzzy system. Experimental example using a nonlinear yo-yo motion control system is analyzed by proposed approach.
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Herrera, B.M., Ribas, L., Coelho, L.d. (2006). Nonlinear Identification Method of a Yo-yo System Using Fuzzy Model and Fast Particle Swarm Optimisation. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_24
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DOI: https://doi.org/10.1007/3-540-31662-0_24
Publisher Name: Springer, Berlin, Heidelberg
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