基于粒子群算法的模拟电路故障诊断方法

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Abstract Based on wavelet packet decomposition, principal component analysis (PCA), normalization, particle swarm optimization (PSO) and neural networks (NNs), a new analog circuit diagnosis method is proposed. The proposed method is based on wavelet packet transformation that using the wavelet packet decomposition as a de-noise tool, the feature information is extracted by wavelet de-noising, multi-resolution, orthogonalization and normalization. The input patterns are satisfied when the feature information applies to the neural networks. Under considering the characteristics of the traditional back-propagation (BP) algorithm, the PSO algorithm is used to substitute the gradient descent method in tradition BP neural network. Finally, the realization of the proposed strategy is expounded by using practical circuits. The simulation and experimental results demonstrate the effectiveness of the proposed method.

Key words： Analog circuits fault diagnosis wavelet packet decomposition particle swarm optimization BP neural network

Received: 11 November 2008 Published: 04 March 2014

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TN39

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	Articles by authors
	He Yigang
	Zhu Wenji
	Zhou Yantao
	Liu Meirong

Cite this article:

He Yigang,Zhu Wenji,Zhou Yantao等. An Analog Circuit Diagnosis Method Based on Particle Swarm Optimization Algorithm[J]. Transactions of China Electrotechnical Society, 2010, 25(6): 163-171.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2010/V25/I6/163

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