Hierarchical genetic fuzzy systems

Abstract This paper introduces a hierarchical evolutionary approach to optimize the parameters of Takagi–Sugeno (TS) fuzzy systems. The approach includes a least-squares method to determine the parameters of nonlinear consequents. A pruning procedure is developed to avoid redundancy in each rule consequent and to achieve proper representation flexibility. The performance of the hierarchical evolutionary approach is evaluated using function approximation and classification problems. They demonstrate that the evolutionary algorithm, working together with optimization and pruning procedures, provides structurally simple fuzzy systems whose performance seems to be better than the ones produced by alternative approaches.

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