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A hybrid evolutionary learning algorithm for TSK-type fuzzy model design. (English) Zbl 1145.93371

Summary: In this paper, a TSK-type Fuzzy Model (TFM) with a Hybrid Evolutionary Learning Algorithm (HELA) is proposed. The proposed HELA method combines the Compact Genetic Algorithm (CGA) and the modified variable-length genetic algorithm. Both the number of fuzzy rules and the adjustable parameters in the TFM are designed concurrently by the HELA method. In the proposed HELA method, individuals of the same length constitute the same group, and there are multiple groups in a population. Moreover, the proposed HELA adopts the CGA to carry out the elite-based reproduction strategy. The CGA represents a population as a probability distribution over the set of solutions and is operationally equivalent to the order-one behavior of the simple GA. The evolution processes of a population consist of three major operations: group reproduction using the compact genetic algorithm, variable two-part individual crossover, and variable two-part mutation. Computer simulations have demonstrated that the proposed HELA method gives a better performance than some existing methods.

MSC:

93C42 Fuzzy control/observation systems
90C59 Approximation methods and heuristics in mathematical programming
68T05 Learning and adaptive systems in artificial intelligence

Software:

Genocop; ANFIS
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References:

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