Application of DE-ELM in analog circuit fault diagnosis

Lihua Zhang, Qi Qin, Yue Shang, Shaowei Chen, Shuai Zhao

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12 Citationer (Scopus)

Abstract

The extreme learning machine (ELM) possesses the advantageous features of the fast learning speed, great generalization performance and high precision. However, the randomness of the parameters will influence its generalization performance and precision greatly. This paper proposes a learning algorithm which is based on the differential evolution extreme learning machine (DE-ELM) for parameter optimization of ELM. It can optimize two parameters, input weights and threshold value, which are random-generated in the network. The experiment selects the elliptic filter circuit to build the fault model. We extract the information of the fault samples using the wavelet packet transformation, then compress the data with the method of principal component analysis. Finally, the DE is applied to optimize the parameters of ELM. The results verified that the proposed method significantly enhances the accuracy of the diagnosis.

OriginalsprogEngelsk
TitelProceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016
RedaktørerQiang Miao, Zhaojun Li, Ming J. Zuo, Liudong Xing, Zhigang Tian
ForlagIEEE Signal Processing Society
Publikationsdato16 jan. 2017
Artikelnummer7819874
ISBN (Elektronisk)9781509027781
DOI
StatusUdgivet - 16 jan. 2017
Udgivet eksterntJa
Begivenhed7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016 - Chengdu, Sichuan, Kina
Varighed: 19 okt. 201621 okt. 2016

Konference

Konference7th IEEE Prognostics and System Health Management Conference, PHM-Chengdu 2016
Land/OmrådeKina
ByChengdu, Sichuan
Periode19/10/201621/10/2016
NavnProceedings of 2016 Prognostics and System Health Management Conference, PHM-Chengdu 2016

Bibliografisk note

Publisher Copyright:
© 2016 IEEE.

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