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 |
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ページ(範囲) | 1870-1877 |
ページ数 | 8 |
ジャーナル | Neurocomputing |
巻 | 72 |
号 | 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