Recurrent type ANFIS using local search technique for time series prediction

This paper presents an improved adaptive neuro-fuzzy inference system (ANFIS) for the application of time series prediction. Because ANFIS is based on a feedforward network structure, it is limited to static problem and cannot effectively cope with dynamic properties such as the time series data. To overcome this problem, an improved version of ANFIS is proposed by introducing self-feedback connections that model the temporal dependence. A batch type local search is suggested to train the proposed system. The effectiveness of the proposed system is tested by using two benchmark time series examples and comparison with the various models in time series prediction is also shown. The results obtained from the simulation show an improved performance.