A Study And Application Of T_S Fuzzy And Neural Network Based On A Clustering Algorithm

T_S model is a novel fuzzy reasoning model which replaces the parameters of traditional reasoning system with linear partial equation.Therefore, it can generate complicated nonlinear equation with fewer fuzzy rules. But the conclusion parameter is not a fuzzy rule but a linear equation, it can not be got from expert's experience and operational data directly. We must refine the parameter with some algorithms. By constructing T_S fuzzy neural networks we can solve the problem easily. But this model can't change the weakness in essence that it is difficult to withdraw the rules, optimize the input space and the construction of system must be designated in advance. The model's application and development are restricted for this reason.In this paper we try to solve the problem. We put clustering algorithm into T_S fuzzy neural networks and withdraw system's character, optimize the input space in this method in order to set up a self-adaptive model. Firstly this paper analyzes and compares some types of typical clustering algorithms, and an improved algorithm is developed referring to this weakness in the meanwhile. The closest clustering algorithm is very simple and has a low wrong rate. In order to simplify the model, reduce calculation and improve the system's real-time responding capacity, we construct the system in this method. Firstly this paper consider the influence of sample's outputs upon the model. Secondly the paper combines the KPCM algorithm and the GA. Finally we construct a model based on T_S Fuzzy and Neural networks.This model can get rule's number, confirm system construction and initialize original parameters according to clustering result automatically. The result parameters can be adjusted by simplex method algorithm. We apply above method into the Iris data samples to evaluate its performance. In the process we can draw the conclusion that the new algorithm is better than the conditional one in adaptation, computational complexity and the modeling accuracy.