Neuro-fuzzy modeling of complex systems using genetic algorithms

In this paper, a genetic-based neuro-fuzzy approach is proposed to build and optimize fuzzy models. The learning algorithm of the fuzzy-neural network is divided into three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed to find the linguistic fuzzy rules. In the third phase, a new technique is used to apply a genetic algorithm to tune the membership functions of the fuzzy model optimally. A well-known example is used to investigate the performance of the proposed modeling approach, and compare it with the other modeling approaches.