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Integrating Fuzzy Systems with Optimization Algorithm

Huilin Ren, Jin Gou

Abstract


In this paper, a fuzzy system fusion structure that can integrate multiple fuzzy systems is proposed. The novel approach is made up of three phases: fuzzy knowledge encoding, the initial fuzzy rule bases acquisition and fuzzy system integration. In the fuzzy knowledge encoding stage, the data set is coded as positive integer according to encoding strategy. The initial fuzzy rule bases are derived from expert groups, evolutionary algorithms or other methods. In the fuzzy system integration phrase, an optimal fuzzy system or an almost optimal fuzzy system is derived from the initial population by using the multi-mutation particle swarm optimization algorithm. Experimental evaluation on different kinds of methods shows that our proposed algorithm can improve the performance of the fusion fuzzy system.


Keywords


fuzzy system, optimization algorithm, pso.

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