摘要
将粗糙集理论与人工神经网络相结合,主要研究柴油机故障特征的提取与优化等问题,目的在于优化、缩减神经网络的输入向量,缩短网络的训练和执行时间,最终实现提高诊断的准确率与效率。
For study problems of extracting characteristics of the parameters of Diesel Engine Fault Diagnosis to achieve optimal testing points,this paper combined together the rough set theory and artificial neural networks,As aresult of optimizing and reduce input vector of the nerve network and cutting down the network of training and execution time,finally improve the accuracy and efficiency of the diesel engine fault diagnosis.
出处
《机械管理开发》
2012年第1期74-75,共2页
Mechanical Management and Development
基金
教育部博士点基金资助项目(20091420110002)
关键词
粗糙集理论
人工神经网络
柴油机
故障诊断
rough set theory
artificial neural networks
the diesel engine
fault diagnosis
