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Stefan cel Mare
University of Suceava
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ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2022 - 9

Analog Circuit Fault Classification and Data Reduction Using PCA-ANFIS Technique Aided by K-means Clustering Approach

LAIDANI, I. See more information about LAIDANI, I. on SCOPUS See more information about LAIDANI, I. on IEEExplore See more information about LAIDANI, I. on Web of Science, BOUROUBA, N. See more information about BOUROUBA, N. on SCOPUS See more information about BOUROUBA, N. on SCOPUS See more information about BOUROUBA, N. on Web of Science
 
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Download PDF pdficon (1,457 KB) | Citation | Downloads: 646 | Views: 806

Author keywords
analog integrated circuits, artificial neural networks, fault diagnosis, fuzzy logic, clustering methods

References keywords
analog(18), fault(17), diagnosis(14), circuits(13), circuit(9), fuzzy(7), electronic(6), method(5), classifier(5), approach(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-11-30
Volume 22, Issue 4, Year 2022, On page(s): 73 - 82
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.04009
Web of Science Accession Number: 000920289700009
SCOPUS ID: 85150155847

Abstract
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The paper work aims to extract effectively the fault feature information of analog integrated circuits and to improve the performance of a fault classification process. Thus, a fault classification method based on principal component analysis (PCA) and adaptive neuro fuzzy inference system classifier (ANFIS) preprocessed by K-means clustering (KMC) is proposed. To effectively extract and select fault features the traditional signal processing based on sampling technique conducts to different signature parameters. A stimulus pulse signal applied to the circuit under test (CUT) allowed us to get a reference output response. Respecting both specific sampling interval and step, the fault free and the faulty output responses are sampled to create amplitude sample features that will serve the fault classification process. The PCA employed for data reduction has lessened the computational complexity and obtaining the optimal features. Thus more than 75% of data volume decreased without loss of original information. The principal components extracted by this reduction data method have been input into ANFIS aided by KMC to obtain the best fault diagnosis results. The experimental results show a score of 100% diagnostic accuracies for the CUTs. Therefore, our approach has achieved best fault classification precision comparing to other research works.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef]


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References Weight

Web of Science® Citations for all references: 14,407 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 534 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-02-27 01:28 in 132 seconds.




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