Optimal Learning of Neo-Fuzzy Structure Using Particle Swarm Optimization
Dong Hwa Kim · 한국정보기술학회논문지 · 2005
Fuzzy logic, neural network, fuzzy-neural network play an important as the key technology of linguistic modeling for intelligent control and decision making in complex systems. The fuzzy-neural network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes immune algorithm based optimal learning fuzzy-neural network (IM-FNN). The proposed learning scheme is the fuzzy-neural network structure which can handle linguistic knowledge as tuning membership function of fuzzy logic by immune algorithm. The learning algorithm of the IM-FNN is composed of two phases. The first phase is to find the initial membership functions of the fuzzy neural network model. In the second phase, immune algorithm is used for tuning of membership functions of the proposed model.