| 引用本文: | 赵 翔,萧德云.基于模块化模糊神经网络的非线性系统故障诊断(英文)[J].控制理论与应用,2001,18(3):395~400.[点击复制] |
| ZHAO Xiang,XIAO De-yun.Fault Diagnosis of Nonlinear Systems Based on Modular Fuzzy Neural Networks[J].Control Theory and Technology,2001,18(3):395~400.[点击复制] |
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| 基于模块化模糊神经网络的非线性系统故障诊断(英文) |
| Fault Diagnosis of Nonlinear Systems Based on Modular Fuzzy Neural Networks |
| 摘要点击 1515 全文点击 1427 投稿时间：1999-11-22 修订日期：2001-01-10 |
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| DOI编号 10.7641/j.issn.1000-8152.2001.3.015 |
| 2001,18(3):395-400 |
| 中文关键词 故障诊断 模糊神经网络 聚类分析 模糊c-均值聚类 |
| 英文关键词 fault diagnosis fuzzy neural network cluster analysis fuzzy c-means clustering |
| 基金项目 |
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| 中文摘要 |
| 提出一种基于模块化模糊神经网络的非线性系统故障诊断新方法. 该方法先使用模糊c-均值聚类法对测量空间进行模块分割, 再利用模糊IF-THEN规则对分割后的子空间分别采用局部BP模型进行逼近. 最后, 通过离线学习获得不同子空间故障输出与测量输入的非线性动力特性. 试验表明该网络具有良好的泛化性能, 可显著提高非线性系统故障检测的快速性、鲁棒性及准确率. |
| 英文摘要 |
| A new approach to fault diagnosis based on modular fuzzy neural networks for nonlinear systems is proposed. Firstly,the measurement space has been divided into several subspaces by using fuzzy c-means clustering. Secondly, according to the requirements of fuzzy rules, the subspaces have been fitted by local BP network respectively. Lastly, the characteristics between fault outputs and measuring inputs in different subspaces have been obtained by processing off line learning. Testing shows the network has good generalization performance and can distinctly improve the speediness,robustness and validity of fault diagnosis in nonlinear systems. |