ANFIS Modelling of Nonlinear System Based on Subtractive Clustering and Combined FFD-Vfold Technique

Abstract

In this paper we have proposed an adaptive network based fuzzy inference system (ANFIS) model where the number of data pairs employed for training is minimized by application of two techniques called the full factorial design (FFD) and V-fold. Our proposed method is applied in building ANFIS models for the benchmark example of Box and Jenkins gas furnace data 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 for 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