基于IWO-PSO优化支持向量机的模拟电路故障诊断

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为提高模拟电路故障诊断率,提出一种基于IWO-PSO优化支持向量机的电路故障诊断方法。通过对典型电路进行Monte-Carlo分析,提取输出端时域信号,经小波包提取特征参量,生成样本数据,再经IWO-PSO改进入侵杂草算法,优化多核SVM参数后建立相应故障诊断模型。实验表明,该模型能较好实现地电路故障诊断模拟,与已有方法相比,可获得较高的故障诊断正确率。

In order to improve the fault diagnosis rate of analog circuits,a circuit fault diagnosis method based on IWO-PSO optimization support vector machine is proposed in this paper.Through the Monte-Carlo analysis of typical circuit,the output time domain signal and the characteristic parameters are extracted by wavelet packet,sample data are generated,and then the multi-kernel SVM parameters through IWO-PSO are optimized to improve the invasive weed algorithm and the corresponding fault diagnosis model is established.The experimental case shows that the fault diagnosis model can achieve the analog circuit fault diagnosis better.Compared with the existing methods,the fault diagnosis model established by this method can achieve higher accuracy of fault diagnosis.