Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm

TL;DR

This paper introduces a genetic algorithm-based method for automatically constructing optimal fuzzy models by learning rules and membership functions, utilizing statistical criteria to reduce rule complexity and improve accuracy.

Contribution

It presents a novel genetic approach that integrates statistical information criteria to automate fuzzy model design and rule reduction, enhancing model accuracy and simplicity.

Findings

Genetic algorithm effectively learns fuzzy rules and membership functions.

Statistical criteria improve fuzzy model optimality and reduce rule count.

Constructed fuzzy controller demonstrates strong performance in simulations.

Abstract

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.