Batch type local search-based adaptive neuro-fuzzy inference system (ANFIS) with self-feedbacks for time-series prediction

Authors:
Catherine Vairappan;Hiroki Tamura;Shangce Gao;Zheng Tang
Affiliations:
Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan;Faculty of Engineering, University of Miyazaki, 1-1, Gakuen Kibanadai Nishi, Miyazaki 889-2192, Japan;Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan;Faculty of Engineering, University of Toyama, Toyama-shi 930-8555, Japan
Venue:
Neurocomputing
Year:
2009

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Abstract

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 presented system is tested by using three 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.