遗传算法优化的SVM模拟电路故障诊断方法

Method for Analog Circuit Fault Diagnosis Based on GA Optimized SVM

  • 摘要: 提出了一种利用遗传算法优化的SVM多分类决策树(GADT-SVM)实现模拟电路故障诊断的新方法。介绍了GADT-SVM的设计思想和算法原理;利用传递函数对模拟电路进行建模,并用小波分解提取电路冲激响应的能量分布作为故障特征;使用GADT-SVM对故障特征样本进行分类实现故障诊断。仿真结果表明,与未经优化的DAG-SVM和DT-SVM故障诊断方法相比,该方法可以减小诊断“误差积累”的影响,具有更好的误差控制能力。

     

    Abstract: A new method for analog circuit fault diagnosis is presented based on genetic algorithm optimized support vector machine multi-class decision tree (GADT-SVM). The design idea and algorithm principle of GADT-SVM is introduced firstly; then model of analog circuit is built by transfer function, and fault characteristic is picked-up by wavelet energy distribution of impulse response. Finally, fault samples are recognized by GADT-SVM. Experiment results show that our method can depress error accumulation phenomena of diagnosis and have stronger error control ability compared with the traditional DAG-SVM and DT-SVM.

     

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