自适应狼群算法优化ELM的模拟电路故障诊断

摘要

In order to detect and diagnose faulty components in analog circuits more efficiently, we propose to use the adaptive wolf pack algorithm to optimize the extreme learning machine (ELM). The method includes the adaptive genetic algorithm which effectively selects feature parameters to generate optimal feature subsets. They are then used to construct the samples which are input into the ELM network to classify the faults. Given that the connection weights between the input layer and hidden layer in the ELM network, and the deviation of the hidden layer can affect the learning speed and classification accuracy, we apply our method to optimize them and select the corresponding optimal value, thus improving the training stability of the ELM network and the success rate of fault diagnosis. The specific realization process of these methods is given through the diagnosis of two typical analog circuits, and their fault diagnosis rates are over 99％. Simulation results show that the method has good accuracy and stability for fault diagnosis of analog circuits.%为了能够更加高效地检测和诊断模拟电路中的故障元件, 提出了自适应狼群算法优化极限学习机的方法.该方法采用自适应遗传算法对特征参数进行选择, 从而生成最优特征子集, 然后利用最优特征子集构造样本输入极限学习机ELM网络对故障进行分类.针对极限学习机的输入层和隐含层之间的连接权值、隐含层的偏差都将会使其学习速度和分类正确率受到影响的问题, 采用本文方法对它们进行优化并选择相应的最优值, 提高了极限学习机网络训练的稳定性与故障诊断的成功率.通过2个典型模拟电路的诊断实例, 给出了这些方法的具体实现过程, 故障诊断率均在99％以上.仿真结果表明使用该方法进行模拟电路故障诊断时具有良好的正确率和稳定性.