Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm
This work addresses the need for automatic design in fuzzy logic control, which traditionally relies on heuristic knowledge, making it incremental by combining genetic algorithms with statistical criteria.
The paper tackled the problem of automating fuzzy logic controller design by using a genetic algorithm to learn fuzzy rules and membership function parameters, and employed statistical information criteria to reduce rules and construct optimal models, with computer simulations confirming performance.
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.