A maximum entropy radial basis function network based neuro-fuzzy controller

Abstract

This paper presents a systematic approach to constructing a self-organizing fuzzy controller. The proposed controller is built on a neuro-fuzzy system consisting of a maximum entropy self-organizing net (MESON) and a radial basis function network (RBFN). We develop the corresponding self-organizing algorithms. MESON, a new fuzzy clustering neural network model, combines the ideas of fuzzy membership values for learning rates based on the maximum entropy principle, and the structure and update rules of the Kohonen clustering network (KCN). The strategy proposed in our approach for the update rules of KCN is derived from the fixed-point iteration for the solution of nonlinear equations. This model eliminates the sensitivity to the choice of the initial configuration and yields a dynamic fuzzy clustering solution. MESON is used for the generation of fuzzy rules as well as the construction of RBFN for fuzzy inference.