A novel hybrid learning algorithm for parametric fuzzy CMAC networks and its classification applications

This paper shows fundamentals and applications of the parametric fuzzy cerebellar model articulation controller (P-FCMAC) network. It resembles a neural structure that derived from the Albus CMAC and Takagi–Sugeno–Kang parametric fuzzy inference systems. In this paper, a novel hybrid learning which consists of self-clustering algorithm (SCA) and modified genetic algorithms (MGA) is proposed for solving the classification problems. The SCA scheme is a fast, one-pass algorithm for a dynamic estimation of the number of hypercube cells in an input data space. The clustering technique does not require prior knowledge such as the number of clusters present in a data set. The number of fuzzy hypercube cells and the adjustable parameters in P-FCMAC are designed by the MGA. The MGA uses the sequential-search based efficient generation (SSEG) method to generate an initial population to determine the most efficient mutation points. Illustrative examples were conducted to show the performance and applicability of the proposed model.