ANFIS Modelling of Nonlinear System Based on Subtractive Clustering and V-fold Technique

In this paper we propose a new technique for optimizing training data in adaptive network based fuzzy inference system (ANFIS) model. Here the number of data pairs employed for training is minimized by applying a technique called V-fold. Our proposed method is experimentally validated by applying it to two separate sets of data obtained from the benchmark Box and Jenkins gas furnace data set and the thermal power plant of the North Eastern Electrical Power Corporation Limited (NEEPCO). By employing our proposed method the number of data required for learning in the ANFIS network could be significantly reduced to around one-eighth of that required in the conventional ANFIS method. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It was found that our model compares favourably well with conventional ANFIS model