基于证据加权调整方法的神经网络及其在故障诊断中的应用

提出一种基于加权思想的证据调整方法,解决证据融合理论中的不同证据应具有不同重要性的问题,并把这种方法和基于证据理论的神经网络相结合,形成加权证据网络。仿真结果表明这种网络有很突出性能。说明了这种方法的有效性,并讨论了在故障诊断中的应用。

STUDY OF NEURAL NETWORK BASED ON METHOD OF WEIGHTED BALANCE OF EVIDENCE AND ITS APPLICATION TO FAULT DIAGNOSIS

It is commonly accepted by many researchers that multiple evidence from different sources of different importance or reliability are not equally important when they are combined according to Dempster-Shafer theory, but it is seldom considered in the existent combination methods. A new method is presented to solve this problem, by which the considered evidence are first balanced according to the weighted average of all and then combined. The method is incorporated into a neural network classifier, which is based on Dempster-Shafer theory, to construct a weighted evidence network and the network is applied to mechanical equipment fault diagnosis problem in the followed experiments. The experiment results demonstrate the excellent performance of this network as compared to the improved RBF network; also the validity of the proposed method in improving the combination's accuracy of multiple evidence is proved.