摘要
利用粗糙集理论对知识的约简能力及神经网络的自学习、自适应能力,构建了粗糙集-神经网络故障诊断模型,并对BP和Elman两种神经网络比较分析。仿真结果表明,与BP结合的方法更能简化神经网络结构,减少网络的训练时间,提高故障诊断的准确率。
Considering the reduction ability of rough set theory and the adaptive and self-learning abilities of neural network,a rough set-neural network combinatorial fault diagnosis model is constructed,and Elman and BP neural network are designed and compared in this paper.The simulation results show that the combinatorial method with BP can more simplify the structure of neural network,shorten the training time of the network and improve the diagnostic accuracy.
出处
《微型电脑应用》
2010年第2期55-58,6,共4页
Microcomputer Applications
