基于GAIS-BP网络在故障诊断中的应用

Application of BP network based on GAIS in fault diagnosis

为提高网络故障诊断的速度和正确率,提出一种用高斯人工免疫系统(GAIS)来优化BP神经网络权值的方法。GAIS采用概率模型替代传统的变异和克隆操作,是一种分布估计算法。由于高斯网络能准确描述变量之间的联系,避免破坏较优解(构造模块),故此概率模型引用高斯网络。GAIS结合高斯网络和人工免疫系统(AIS)的优点,提高寻优的收敛速度。UCI数据集和网络实测数据集验证了GAIS-BP网络比GA-BP网络收敛速度更快,正确率更高。

To improve the speed and accuracy of network fault diagnosis, a method of utilizing Gaussian artificial immune system （GAIS） to optimize BP neural network weights is presented. Gaussian Artificial Immune System is an estimation of distribution algorithms that replaces the traditional mutation and cloning operators with a prohabilistic model. The Gaussian network can ac- curately describe the relationships among the variables to avoid the disruption of already obtained high-quality partial solutions （building blocks）, therefore the probability model using Gaussian network. GAIN combine the advantages of Gaussian network and Artificial Immune System （AIS） to improve the convergence speed of optimization. By the UCI data set and network measurement data set, it is verified that the GAIS-BP network have the faster convergence and the higher accuracy than GA-BP network.