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author:

Yan Ren-Wu (Yan Ren-Wu.) [1] | Cai Jin-Ding (Cai Jin-Ding.) [2]

Indexed by:

CPCI-S EI Scopus

Abstract:

This paper presents a novel method of applying auto-regressive(AR) model and random forests to fault diagnosis of power electronic circuit. AR model is used to extract the features of the sample data,realize optimum compressed of fault sample data, simplify the data structure in fault diagnosis, enhance classify speed and precision. By simulating fault status of power electronic circuit,this paper investigates design details of random forests classifier and evaluates its performance. Experimental results show that the method is feasible and effective.

Keyword:

AR model fault diagnosis power electronic circuit random forests

Community:

  • [ 1 ] [Yan Ren-Wu]Fuzhou Univ, Elect Engn & Automatizat Coll, Fuzhou 350002, Peoples R China
  • [ 2 ] [Cai Jin-Ding]Fuzhou Univ, Elect Engn & Automatizat Coll, Fuzhou 350002, Peoples R China

Reprint 's Address:

  • 鄢仁武

    [Yan Ren-Wu]Fuzhou Univ, Elect Engn & Automatizat Coll, Fuzhou 350002, Peoples R China

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Related Article:

Source :

ICIC 2009: SECOND INTERNATIONAL CONFERENCE ON INFORMATION AND COMPUTING SCIENCE, VOL 1, PROCEEDINGS: COMPUTING SCIENCE AND ITS APPLICATION

Year: 2009

Page: 285-288

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 5

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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