摘要
为了提高径向基神经网络(radial basis funtion neural network,RBFNN)进行模拟电路故障诊断的速度与准确性,提出了一种基于粒子群算法(particle swarm optimization,PSO)优化RBFNN的故障诊断方法。该方法利用PSO优化RBFNN的结构参数,克服了神经网络中模型结构和参数难以设置的缺点,避免了参数选择的盲目性;同时对模拟电路的响应信号采用小波包分解,提取有效故障特征。仿真结果表明,方法具有更高的诊断精度和更快的收敛速度,能有效地实施模拟电路的故障定位。
In order to improve the speed and accuracy of analog circuit fault diagnosis using radial basis funtion neural network(RBFNN),this paper proposed a new fault diagnosis method based on RBFNN optimized by particle swarm optimization(PSO).Trained RBFNN by the PSO algorithm which overcame the shortcomings that structure and parameters of neural network were hard to be set.Preprocessed the response signals of analog circuit by wavelet packet transform as the fault feature.The simulation result shows that this method which has higher diagnostic accuracy and faster convergence speed is effective for fault location.
出处
《计算机应用研究》
CSCD
北大核心
2012年第1期72-74,111,共4页
Application Research of Computers
基金
国家自然科学基金资助项目(60973032
60673084)
湖南省自然科学基金重点资助项目(10JJ2045)
关键词
模拟电路
故障诊断
径向基神经网络
粒子群算法
小波包分解
analog circuit
fault diagnosis
radial basis function network
particle swarm optimization
wavelet packet