关键词: 构造型神经网络, 故障诊断, 覆盖, 算法

Abstract: A new fault diagnosis algorithm can be used to resolve the problem of fault diagnosis with prior knowledge more effectively. Taking the prior sample point as the center, using inner product to judge sample data similarity, it carries on the cluster analysis. It makes a super-plane intersect a sphere in the characteristics of the space, obtains a spherical covering area, thus transforms the neural network training question as the set of points cover question. Based on constructive neural networks, the algorithm’s characteristic is that the sample data of fault can be handled directly. Because the cover center is determined, it constructs out the least element hidden layer network structure. This new algorithm can reduce the long training time and learning complexity of traditional neural networks. Computer simulation results confirm the effectiveness of the algorithm.

Key words: constructive neural networks, fault diagnosis, covering, algorithm