Neuro-fuzzy system modeling based on automatic fuzzy clustering

A neuro-fuzzy system model b as ed on automatic fuzzy clustering is proposed.A h ybrid model identification algorithm is also developed to decide the model structure and model parameter s.The algorithm mainly includes three parts:1) Autom atic fuzzy C-means (AFCM),which is applied to ge nerate fuzzy rules automatically,and then fix on the size of the neuro-fuzzy network,by which t he complexity of system design is reducesd greatly a t the price of the fitting capability;2) Recursive least square estimation (RLSE).It is used to update the parameters of Takagi-Sugeno model,which i s employed to describe the behavior of the system;3) Gradient descent algorithm is also proposed for the fuzzy va lues according to the back pro pagation algorithm of neural network.Finally,modeling the dynamical equation of the two-link manipulator with the proposed approach is illustrated to valid ate the feasibility of the method.