Recurrent type ANFIS using local search technique for time series prediction

Hiroki Tamura; Koichi Tanno; Hisasi Tanaka; Catherine Vairappan; Zheng Tang

doi:10.1109/APCCAS.2008.4746039

Recurrent type ANFIS using local search technique for time series prediction

Hiroki Tamura^*, Koichi Tanno, Hisasi Tanaka, Catherine Vairappan, Zheng Tang

^*この論文の責任著者

工学科　知能情報工学コース

研究成果: 書籍の章/レポート/会議録 › 会議への寄与 › 査読

17 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 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.

本文言語	英語
ホスト出版物のタイトル	Proceedings of APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems
ページ	380-383
ページ数	4
DOI	https://doi.org/10.1109/APCCAS.2008.4746039
出版ステータス	出版済み - 2008
イベント	APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems - Macao, 中国継続期間: 2008/11/30 → 2008/12/03

出版物シリーズ

名前	IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS

学会

学会	APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems
国/地域	中国
City	Macao
Period	2008/11/30 → 2008/12/03

ASJC Scopus 主題領域

電子工学および電気工学

文献へのアクセス

10.1109/APCCAS.2008.4746039

引用スタイル

Tamura, H., Tanno, K., Tanaka, H., Vairappan, C., & Tang, Z. (2008). Recurrent type ANFIS using local search technique for time series prediction. In Proceedings of APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems (pp. 380-383). 記事 4746039 (IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS). https://doi.org/10.1109/APCCAS.2008.4746039

@inproceedings{d8bea1e25b0940eb802668e593f11489,

title = "Recurrent type ANFIS using local search technique for time series prediction",

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 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.",

author = "Hiroki Tamura and Koichi Tanno and Hisasi Tanaka and Catherine Vairappan and Zheng Tang",

year = "2008",

doi = "10.1109/APCCAS.2008.4746039",

language = "英語",

isbn = "9781424423422",

series = "IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS",

pages = "380--383",

booktitle = "Proceedings of APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems",

note = "APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems ; Conference date: 30-11-2008 Through 03-12-2008",

}

Tamura, H, Tanno, K, Tanaka, H, Vairappan, C & Tang, Z 2008, Recurrent type ANFIS using local search technique for time series prediction. in Proceedings of APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems., 4746039, IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS, pp. 380-383, APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems, Macao, 中国, 2008/11/30. https://doi.org/10.1109/APCCAS.2008.4746039

Recurrent type ANFIS using local search technique for time series prediction. / Tamura, Hiroki; Tanno, Koichi; Tanaka, Hisasi その他.
Proceedings of APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems. 2008. p. 380-383 4746039 (IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS).

研究成果: 書籍の章/レポート/会議録 › 会議への寄与 › 査読

TY - GEN

T1 - Recurrent type ANFIS using local search technique for time series prediction

AU - Tamura, Hiroki

AU - Tanno, Koichi

AU - Tanaka, Hisasi

AU - Vairappan, Catherine

AU - Tang, Zheng

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

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U2 - 10.1109/APCCAS.2008.4746039

DO - 10.1109/APCCAS.2008.4746039

M3 - 会議への寄与

AN - SCOPUS:62949189121

SN - 9781424423422

T3 - IEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS

SP - 380

EP - 383

BT - Proceedings of APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems

T2 - APCCAS 2008 - 2008 IEEE Asia Pacific Conference on Circuits and Systems

Y2 - 30 November 2008 through 3 December 2008

ER -

Recurrent type ANFIS using local search technique for time series prediction

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出版物シリーズ

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ASJC Scopus 主題領域

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