MVLSC HomeIssue Contents

A Parametric Fuzzy CMAC Model with Hybrid Evolutionary Learning Algorithms
Chi-Yung Lee, Cheng-Jian Lin and Yung-Chi Hsu

This paper shows fundamentals and applications of the parametric fuzzy cerebellar model articulation controller (P-FCMAC) model. It resembles a neural structure that derived from the Albus CMAC algorithm and Takagi-Sugeno-Kang parametric fuzzy inference systems. In this paper, we also propose the hybrid of evolutionary learning algorithm (HELA) to tune the P-FCMAC. The proposed HELA combines the compact genetic algorithm (CGA) and the modified variable-length genetic algorithm (MVGA). Both the number of hypercube cells and the adjustable parameters in P-FCMAC are designed concurrently by the HELA. In the proposed HELA, individuals of the same length constitute the same group, and there are multiple groups in a population. The evolution of a population consists of three major operations: group reproduction using the compact genetic algorithm, variable two-part crossover, and variable two-part mutation. Illustrative examples were conducted to show the performance and applicability of the proposed model and method.

Full Text (IP)