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

Catherine Vairappan*, Hiroki Tamura, Shangce Gao, Zheng Tang

*この論文の責任著者

研究成果: ジャーナルへの寄稿学術論文査読

54 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)1870-1877
ページ数8
ジャーナルNeurocomputing
72
7-9
DOI
出版ステータス出版済み - 2009/03

ASJC Scopus 主題領域

  • コンピュータ サイエンスの応用
  • 認知神経科学
  • 人工知能

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