Modeling Nonlinear Systems by a Fuzzy Logic Neural Network Using Genetic Algorithms

Abstract

The main aim of this work is to optimize the parameters of the constrained membership function of the Fuzzy Logic Neural Network (FLNN). The constraints may be an indirect definition of the search ranges for every membership shape forming parameter based on 2nd order fuzzy set specifications. A particular method widely applicable in solving global optimization problems is introduced. This approach uses a Linear Adapted Genetic Algorithm (LAGA) to optimize the FLNN parameters. In this paper the derivation of a 2nd order fuzzy set is performed for a membership function of Gaussian shape, which is assumed for the neuro-fuzzy approach. The explanation of the optimization method is presented in detail on the basis of two examples.