摘要
利用粗糙集理论对知识的约简能力及模糊神经网络的分类能力,构建粗糙集—神经网络故障诊断组合模型(RNN),具有良好的拓扑结构,学习速度大为提高。应用电力变压器实例验证,RNN模型诊断速度快,故障诊断正确率高。
Considering the reduction ability of rough set theory and the classification ability of fuzzy neural network, a rough set - neural network combinatorial fault-diagnosing model is constructed. The model enjoys a better topological structure and greatly increased speed for learning. The practical application to power transformer verifies that the model has comparably fast and accurate diagnosing abilities.
出处
《煤矿机电》
2008年第1期34-36,共3页
Colliery Mechanical & Electrical Technology
关键词
粗糙集
人工神经网络
故障诊断
粗糙集-神经网络
rough set
artificial neural network
fault diagnosis
rough set-neural network
作者简介
安文斗（1966-），男，高级工程师。2005年毕业于重庆大学电气工程学院（博士学位），现从事矿山电力系统自动化及故障诊断技术的研究，发表论文30多篇。
