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Fuzzy system identification via chaotic ant swarm. (English) Zbl 1198.90419
Summary: We introduce a chaotic optimization method, called CAS (chaotic ant swarm), to solve the problem of designing a fuzzy system to identify dynamical systems. The position vector of each ant in the CAS algorithm corresponds to the parameter vector of the selected fuzzy system. At each learning time step, the CAS algorithm is iterated to give the optimal parameters of fuzzy systems based on the fitness theory. Then the corresponding CAS-designed fuzzy system is built and applied to the identification of the unknown nonlinear dynamical systems. Numerical simulation results are provided to show the effectiveness and feasibility of the developed CAS-designed fuzzy system.
Editorial remark: There are doubts about a proper peer-reviewing procedure of this journal. The editor-in-chief has retired, but, according to a statement of the publisher, articles accepted under his guidance are published without additional control.

MSC:
90C70 Fuzzy and other nonstochastic uncertainty mathematical programming
90C59 Approximation methods and heuristics in mathematical programming
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