Optimal learning of fuzzy neural network using artificial immune algorithm
Fuzzy logic, neural network, fuzzy-neural networks play an important role in the linguistic modeling of intelligent control and decision making in complex systems. The Rizzy-Neural Network (FNN) learning represents one of the most effective algorithms to build such linguistic models. This paper proposes an Artificial Immune Algorithm (AIA) based optimal learning fuzzy-neural network (IM-FNN). The proposed learning scheme includes the discovery of the fuzzy-neural network structure which can handle linguistic knowledge and the tuning of the membership function of the fuzzy inference system is achieved by AIA. 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 the membership functions of the proposed model. This paper also suggests techniques in determining the values of the steady-state equivalent circuit parameters of a three-phase squirrel-cage induction machine using immune algorithm.
fuzzy neural network immune algorithm induction motor parameter estimation