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基于PSpice与LM算法的模拟电路故障诊断方法
资料介绍
采用小波神经网络与Levenberg—Marquardt算法相结合的方法，对模拟电路进行故障诊断；用小波对冲击响应信号进行多尺度分解，进行归一化后，提取故障特征信息作为神经网络的输入而进行分类。将PSpice与Matlab结合不但能有效的诊断模拟电路，且在收敛性和故障准确性上有了大幅提高。实验仿真表明，通过该方法构造的样本集训练出的网络稳定性高于传统方法，适用于神经网络。．
Fault diagnosis of analog circuit is accomplished based on LM algorithm combined with wavelet neural network. After multl-scale decomposition and normalization of impulse response signal, feature information is ex- tracted and categorized as the neural network. The fault diagnosis method achieves efficient identification of analog circuits by combing PSpice and Matlab with significantly improved convergence and fault accuracy. Simulation results show that the network trained with sample set by this method offers better accuracy than those by traditional methods in fault diagnosis of analog circuits, and is applicable to the neural network.
部分文件列表
| 文件名 | 大小 |
| 基于PSpice与LM算法的模拟电路故障诊断方法.pdf | 280K |
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