Construction of fuzzy basis function networks using adaptive least squares method

Abstract

A novel algorithm based on the least squares (LS) method and genetic algorithm (GA) is proposed for autonomous learning and construction of FBFN's when training data are available. The proposed algorithms add significant fuzzy basis functions (FBF) at each iteration during training, based on error reduction measures. The adaptive least squares (ALS) algorithm based on the combined LS and GA, realizes hybrid structure-parameter learning without any human intervention. Simulation studies are performed with numerical examples for comparison with conventional algorithms. The ALS algorithm is applied to the construction of a fuzzy basis function network model for surface roughness in a grinding process using experimental data.