A method for compiling neural networks into fuzzy rules using genetic algorithms and hierarchical approach

Abstract

Neural networks have been criticized for their lack of human comprehensibility, which make them to appear as black box structures to the user. The paper proposes a mechanism that compiles a neural network into an equivalent set of fuzzy rules. Genetic algorithms are used to find the right structure of the fuzzy model equivalent with the neural network, and then to find the best shape of the membership functions. In order to reduce the number of fuzzy rules, we look for a hierarchical structure of the fuzzy system, considering the relations between the network inputs.