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投稿时间：2015-10-20
投稿时间：2015-10-20
中文摘要: 基于对RBF神经网络常用的3种学习算法的研究，通过对凝汽器典型故障类型与故障征兆分析，提出了基于不同学习算法的RBF神经网络凝汽器故障诊断，并对诊断结果进行比较.诊断结果表明，基于3种常见学习算法的RBF神经网络都可以准确诊断出凝汽器的各种故障，但聚类方法和OLS算法学习速度要快得多，梯度训练方法速度较慢.研究还表明，RBF神经网络在故障诊断领域具有很好的实用性.
Abstract:Through the research of three kinds of learning algorithms for RBF neural network and analysis of typical faults and symptoms of the condenser,RBF neural network based on different learning algorithms for the condenser fault diagnosis is presented.Finally,the result of the diagnosis is compared.Diagnosis results show RBF neural network based on three kinds of learning algorithms can accurately diagnose various fault diagnosis of the condenser.But the study speed of the clustering method and the OLS algorithm is faster;on the contrary,the gradient training method is slower.The research also shows that the RBF neural network has good practicability in the field of fault diagnosis.
文章编号：201600617 中图分类号： 文献标志码：
基金项目:
| 作者 | 单位 | |
| 李玲 | 上海电力学院 自动化工程学院 | liling@shiep.edu.cn |
| 胡克磊 | 安徽华电六安电厂有限公司 热控分厂 |
引用文本：
李玲,胡克磊.基于不同学习算法的RBF神经网络在故障诊断中的应用[J].上海电力大学学报,2016,32(6):583-588,602.
LI Ling,HU Kelei.Application of Fault Diagnosis Based on RBF Neural Network with Different Learning Algorithms[J].Journal of Shanghai University of Electric Power,2016,32(6):583-588,602.
李玲,胡克磊.基于不同学习算法的RBF神经网络在故障诊断中的应用[J].上海电力大学学报,2016,32(6):583-588,602.
LI Ling,HU Kelei.Application of Fault Diagnosis Based on RBF Neural Network with Different Learning Algorithms[J].Journal of Shanghai University of Electric Power,2016,32(6):583-588,602.