Least Squares Support Vector Machine for Fault Diagnosis Optimization

Si Yang Liang; Jian Hong Lv

doi:10.4028/www.scientific.net/AMM.347-350.505

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350Least Squares Support Vector Machine for Fault...

Least Squares Support Vector Machine for Fault Diagnosis Optimization

Abstract:

In order to improve the diagnostic accuracy of digital circuit, the fault diagnosis method based on support vector machines (SVM) is proposed. The input is fault characteristics of digital circuit; the output is the fault style. The connection of fault characteristics and style was established. Network learning algorithm using least squares, the training sample data is formed by the simulation, the test sample data is formed by the untrained simulation. The method achieved the classification of faulted digital circuits, and the results show that the method has the features of fast and high accuracy.

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Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

505-508

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.505

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Online since:

August 2013

Authors:

Si Yang Liang, Jian Hong Lv

Keywords:

Digital Circuit, Fault Diagnosis, Least Squares Support Vector Machines (Ls-SVM)

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