An adaptive learning algorithm for T-S fuzzy model based RBF neural network

An adaptive learning algorithm for T-S fuzzy model based RBF neural network is developed,with which the difficulties due to enormous fuzzy inference rules as well as parameter identification in multi-dimension fuzzy inferences can be overcome. The number of hidden layer nodes of T-S fuzzy RBF net is not only modified dynamically but also the position of RBF net data centers is changed adaptively during learning progress, so that the algorithm has better self-learning and generalization ability. Simulation results show that the algorithm is effective and feasible, and any nonlinear function can be approximated with any required degree of accuracy.