自适应GA优化WNN的模拟电路软故障诊断方法  

Adaptive genetic algorithm optimized WNN approach for analog circuit soft fault diagnosis

作　　者：谢春[1] 宋国明[1] 姜书艳[2] 王厚军[2] 

机构地区：[1]成都电子机械高等专科学校计算机工程系,成都610031 [2]电子科技大学自动化工程学院,成都610054

出　　处：《计算机应用研究》2012年第1期75-78,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(60971036);国防基础科研资助项目(A1420061264)

摘　　要：在小波神经网络(WNN)的模拟电路故障诊断系统中,普遍采用的梯度下降算法在训练时易使网络陷入局部最优,而网络结构的冗余也会造成训练收敛方向偏离全局最优点,降低推广能力和增加误诊率。用自适应遗传算法优化WNN,以克服上述缺陷。采用该方法可简化小波神经网络的结构和优化参数,在滤波器电路的软故障识别中获得满意的效果。与常规的WNN故障诊断方法相比,有效地提高了故障诊断的效率和正确率。In analog circuit fault diagnosis system using wavelet neural networks（WNN）,the prevalent algorithm,gradient descent algorithm,is prone to make WNN converge to the local minimum in training phase.Additionally,the structure redundancy of network may lead to training convergence direction deviating from globally optimal point so that the network gegenerality will be degraded and diagnosis inaccuracy increased.This paper proposed the adaptive genetic algorithm for optimizing WNN to avoid the limitation above.This approach could achieve simplified structure and optimized parameters for WNN,which obtained satisfactory effects in soft fault identification for filter circuit.The presented method gained better diagnosis efficiency and accuracy in comparison with conventional WNN approach.

关 键 词：模拟电路 故障诊断 自适应遗传算法 优化 小波神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN707[自动化与计算机技术—控制科学与工程]