V-fold Technique based ANFIS Model for Complex Large - Scale Systems

Abstract

Adaptive Network based Fuzzy Inference System (ANFIS) is an intelligent neuro-fuzzy technique used for modelling and control of ill-defined and uncertain systems. ANFIS is based on the input-output data pairs of the system under consideration. The size of the input-output data set is very crucial when the data available is very less and the generation of data is a costly affair. Under such circumstances, optimization in the number of data used for learning is of prime concern. In this paper we have proposed an ANFIS based system modelling where the number of data pairs employed for training is minimized by application of a technique called the V-fold technique. Our proposed method is used for modelling of two complex large-scale systems. One input-output data set is taken from the benchmark example of Box and Jenkins gas furnace data (1970). The other set of real time-series data used is collected from a thermal power plant. It is found that 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. This also results in saving of the computation time as well as computation complexity. The results obtained by applying our proposed method are compared with those obtained by using conventional ANFIS network. It is observed that our proposed model built by using V-fold technique based ANFIS performs comparably with conventional ANFIS model.