Bootstrap feature extraction technique for neural network based ATC assessment

M. M. Othman; A. Mohamed; A. Hussein

Bootstrap feature extraction technique for neural network based ATC assessment

M. M. Othman
, A. Mohamed
, A. Hussein

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper presents a bootstrap feature extraction technique which is used to intensify the generalizing ability of neural network to accurately quantify the interarea available transfer capability (ATC). For ATC assessment, the neural network method with the Levenberg-Marquardt modified back-propagation algorithm is used. To investigate the effectiveness of the proposed bootstrap feature extraction technique, a comparison is made with discrete Fourier transform and sensitivity techniques as a means of extracting or selecting the neural network input features. A case study is performed on the Malaysian power system to illustrate the effectiveness of the proposed bootstrap technique.

Original language	English
Title of host publication	IPEC 2003 - 6th International Power Engineering Conference
Pages	865-870
Number of pages	6
State	Published - 2003
Externally published	Yes
Event	IPEC 2003 - 6th International Power Engineering Conference - , Singapore Duration: 27 Nov 2003 → 29 Nov 2003

Publication series

Name	IPEC 2003 - 6th International Power Engineering Conference

Conference

Conference	IPEC 2003 - 6th International Power Engineering Conference
Country/Territory	Singapore
Period	27/11/03 → 29/11/03

Keywords

Artificial neural network
Available transfer capability
Bootstrap feature extraction

ASJC Scopus subject areas

General Engineering

Cite this

@inproceedings{201c0014416e4f0a9902c6e5747b1c65,

title = "Bootstrap feature extraction technique for neural network based ATC assessment",

abstract = "This paper presents a bootstrap feature extraction technique which is used to intensify the generalizing ability of neural network to accurately quantify the interarea available transfer capability (ATC). For ATC assessment, the neural network method with the Levenberg-Marquardt modified back-propagation algorithm is used. To investigate the effectiveness of the proposed bootstrap feature extraction technique, a comparison is made with discrete Fourier transform and sensitivity techniques as a means of extracting or selecting the neural network input features. A case study is performed on the Malaysian power system to illustrate the effectiveness of the proposed bootstrap technique.",

keywords = "Artificial neural network, Available transfer capability, Bootstrap feature extraction",

author = "Othman, \{M. M.\} and A. Mohamed and A. Hussein",

year = "2003",

language = "English",

isbn = "9810487053",

series = "IPEC 2003 - 6th International Power Engineering Conference",

pages = "865--870",

booktitle = "IPEC 2003 - 6th International Power Engineering Conference",

note = "IPEC 2003 - 6th International Power Engineering Conference ; Conference date: 27-11-2003 Through 29-11-2003",

}

Bootstrap feature extraction technique for neural network based ATC assessment. / Othman, M. M.; Mohamed, A.; Hussein, A.
IPEC 2003 - 6th International Power Engineering Conference. 2003. p. 865-870 P1129 (IPEC 2003 - 6th International Power Engineering Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Bootstrap feature extraction technique for neural network based ATC assessment

AU - Othman, M. M.

AU - Mohamed, A.

AU - Hussein, A.

PY - 2003

Y1 - 2003

N2 - This paper presents a bootstrap feature extraction technique which is used to intensify the generalizing ability of neural network to accurately quantify the interarea available transfer capability (ATC). For ATC assessment, the neural network method with the Levenberg-Marquardt modified back-propagation algorithm is used. To investigate the effectiveness of the proposed bootstrap feature extraction technique, a comparison is made with discrete Fourier transform and sensitivity techniques as a means of extracting or selecting the neural network input features. A case study is performed on the Malaysian power system to illustrate the effectiveness of the proposed bootstrap technique.

AB - This paper presents a bootstrap feature extraction technique which is used to intensify the generalizing ability of neural network to accurately quantify the interarea available transfer capability (ATC). For ATC assessment, the neural network method with the Levenberg-Marquardt modified back-propagation algorithm is used. To investigate the effectiveness of the proposed bootstrap feature extraction technique, a comparison is made with discrete Fourier transform and sensitivity techniques as a means of extracting or selecting the neural network input features. A case study is performed on the Malaysian power system to illustrate the effectiveness of the proposed bootstrap technique.

KW - Artificial neural network

KW - Available transfer capability

KW - Bootstrap feature extraction

UR - https://www.scopus.com/pages/publications/12744278295

M3 - Conference contribution

AN - SCOPUS:12744278295

SN - 9810487053

SN - 9789810487058

T3 - IPEC 2003 - 6th International Power Engineering Conference

SP - 865

EP - 870

BT - IPEC 2003 - 6th International Power Engineering Conference

T2 - IPEC 2003 - 6th International Power Engineering Conference

Y2 - 27 November 2003 through 29 November 2003

ER -

Bootstrap feature extraction technique for neural network based ATC assessment

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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