改进椭球单元神经网络方法在多故障诊断中的应用

MUTI FAULT DIAGNOSIS WITH AN IMPROVE NEURAL NETWORKS WITH ELLIPSOIDAL ACTIVATION FUNCTIONS

椭球单元网络具有分类精度高，训练速度快，对于未知模式有良好的“拒绝”性能。文章提出了改进椭球单元神经网络的训练权重组，采用多权重组，提高了网络的故障诊断能力；利用椭球单元网络组合多个网络进行诊断，将多故障诊断任务分解，提高了诊断能力。

Neural Networks with Ellipsoidal Activation Functions close in upon a decision making region by Gauss distribution for various patterns and are adapted for fault diagnosis well.It has the advantages,such as high precision in clssifying,few training time and well rejection for unknown pattern.For improving the anti jamming performance,a multi weight is adopted between input neuron and ellipsoidal neuron in the paper.The selection method of weights is also derived.By combining several neural networks with ellipsoidal activation functions,the multi fault diagnosis can be diagnosed.the scale of network and training time are reduced observably and the ability for diagnosis is enhanced by the combination of networks.The simulation and experiments sustain the suggested method.