粗糙集——神经网络故障诊断方法研究
首发时间:2008-12-02
摘要: 粗糙集理论具有处理不完整样本数据的优点,本文提出了使用粗糙集理论优化神经网络故障诊断系统的算法。在保证故障分类结果基本不变的情况下,利用粗糙集理论对原始故障特征信息表进行约简,得到最简决策表,导出诊断规则, 再输入神经网络进行训练学习。通过一个具体实例分析表明,该方法有效地减少了输入层神经元的个数,提高了网络的学习速率和诊断的准确率,在故障诊断中有良好的应用前景。
For information in English, please click here
Research on Rough Set-Neural Network Fault Diagnosis Method
Abstract:Rough set theory (RST in short) can deal with incomplete data, this paper propose the algorithm of using rough set theory to optimize neural network in fault diagnosis system. Not changing classification ability basically, RST is used to simplify the original fault information sheet, get the simplest expert diagnosis rules, and then enter the neural network for training and learning. At last, through a concrete example, the experiment result shows that this method can effectively reduce the input neurons number and improve the network\\\\\\\
Keywords: Rough Set,Fault Diagnosis,Discernibility Matrix,Neural Network,Expert System
论文图表:
引用
No.2624132379612282****
同行评议
共计0人参与
勘误表
粗糙集——神经网络故障诊断方法研究
评论
全部评论0/1000