Adaptive Fuzzy Inference System using Pruning Techniques

Fuzzy modelling has the approximation property far the given input-output relationship. Especially, Takagi-Sugeno fuzzy models are widely used because they show very good performance in the nonlinear function approximation problem. But generally there is not the systematic method incorporating the human expert's knowledge or experience in fuzzy rules and it is not easy to End the membership function of fuzzy rule to minimize the output error as well. The ANFIS (Adaptive Network-based Fuzzy Inference Systems) is one of the neural network based fuzzy modelling methods that can be used with various type of fuzzy rules. But in this model, it is the problem to End the optimum number of fuzzy rules in fuzzy model. In this paper, a new fuzzy modelling method based on the ANFIS and pruning techniques with the measure named impact factor is proposed and the performance of proposed method is evaluated with several simulation results.