Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm

Details Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm

2012-01-10

arxiv.org/abs/1201.2004v1

Computer Science

Artificial Intelligence

Scientific paper

10.5121/ijcsit.2011.3619

Fuzzy rule based models have a capability to approximate any continuous function to any degree of accuracy on a compact domain. The majority of FLC design process relies on heuristic knowledge of experience operators. In order to make the design process automatic we present a genetic approach to learn fuzzy rules as well as membership function parameters. Moreover, several statistical information criteria such as the Akaike information criterion (AIC), the Bhansali-Downham information criterion (BDIC), and the Schwarz-Rissanen information criterion (SRIC) are used to construct optimal fuzzy models by reducing fuzzy rules. A genetic scheme is used to design Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule parameters and the identification of the consequent parameters. Computer simulations are presented confirming the performance of the constructed fuzzy logic controller.

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