基于自适应神经网络的电路系统故障诊断研究

针对人工神经网络的特点，对传统BP算法进行了全面改进，通过采用自适应学习率和动量因子修正等方法，有效克服了传统BP算法在实际应用中学习收敛速度慢和容易出现局部极小点的缺点。以电路系统的故障诊断为例，引入了模糊数学中的隶属度函数，对故障特征量进行处理后作为自适应神经网络的输入，故障编码作为网络的输出。实验仿真结果表明，该系统对电路故障类型能够有效地进行诊断和识别。

Research on Fault Diagnosis of Circuit System Based on Adaptive Neural Network

The traditional back-propagation algorithm has some disadvantages,for instance,the speed of learning convergence is too slow and local extreme values are present in the process of search sometimes.In order to solve the problems described above,this paper applies adaptive learning rate and momentum term to improve the general back-propagation algorithm.Taking the circuit system's fault diagnosis for example,the basic fault features are transformed by means of using the fuzzy sets of fuzzy mathematics,and take as the inputs of the adaptive neural network.And then,the fault codes are used as the outputs of adaptive neural network.The experiment simulation results show that this method can be used to diagnose and identify the fault types of circuit system effectively.