基于粒子群支持向量机的模拟电路故障诊断

针对传统神经网络技术在模拟电路故障应用中存在的问题，提出了一种基于粒子群算法(particle swarm optimization, PSO)和最小二乘支持向量机(least squares support vector machine, LSSVM)的模拟电路故障诊断的方法。该方法首先利用小波包技术对待诊断电路的可测点信息提取故障特征，然后使用粒子群算法优化支持向量机的结构参数，避免了参数选择的盲目性，提高了模型的诊断精度。在对某滤波电路进行的故障检测中，验证了该方法的可行性。

Analog circuit fault diagnosis based on particle swarm optimization support vector machine

In order to solve the problem of fault diagnosis method for analog IC diagnosis based on neural network, the method based on particle swarm optimization (PSO) and least squares support vector machine (LSSVM) is proposed. Wavelet package is used as a tool for extracting feature. Then, after training the LSSVM by PSO, the model of the circuit with fault diagnosis system is built. Simulation results show that the method is more effective.