摘要
针对网络故障诊断过程中故障规则难以提取的问题,提出一种基于改进BP神经网络的故障诊断方法。以网络故障信息为样本对BP网络进行训练,利用其强大的自适应能力和非线性映射能力,建立起网络故障信息与故障模式输出之间的映射。同时,为了避免BP网络的学习算法陷入局部极小值,提高故障诊断的效率和精确度,采用L-M优化算法来对网络进行训练。另外,采取初期终止的方法提高BP网络的泛化能力。实例表明,该方法有效提高了网络故障诊断的有效性。
To solve the problem that network fault diagnosis rules are hard to get,a fault diagnosis method based on improved BP neural network is presented.The fault information is taken as samples to train the BP neural network,based on that,the relations between the network fault information and fault patterns are established utilizing the self-adaptation and nonlinearity mapping functions of the neural network.At the same time,Levenberg-Marquardt algorithm is applied to avoid that the learning algorithm of BP neural gets into local minimum,and to improve the efficiency and accuracy of the network.In addition,the thought of early-stop is adopted to improve the generation of the BP neural network.Finally,a practical example indicates the feasibility and validity of the method.
出处
《计算机与数字工程》
2012年第2期65-67,共3页
Computer & Digital Engineering
