Analog Circuits Based Fault Diagnosis using ANN and SVM

Archana Dhamotharan, Kanthalakshmi Srinivasan
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Abstract

In this study, we provide a technique for identifying analog errors using a neural network and an SVM (SVM). The study's major objective is to produce a trustworthy diagnostic based on a technique that reduces testing durations by resolving the problem of component tolerances.The suggested strategy uses an artificial neural network and a backward propagation mechanism. The impact of methods like Principal Component Analysis on feature extraction is discussed in this work. The simulation results show that the technique is effective and efficient for fault identification in tolerant mixed-signal circuits.
基于神经网络和支持向量机的模拟电路故障诊断
在这项研究中,我们提供了一种使用神经网络和支持向量机(SVM)识别模拟误差的技术。该研究的主要目标是基于一种技术,通过解决组件公差问题来减少测试时间,从而产生一种值得信赖的诊断方法。该策略采用人工神经网络和反向传播机制。本文讨论了主成分分析等方法对特征提取的影响。仿真结果表明,该方法对容错混合信号电路的故障识别是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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