| 一种用于动态过程故障诊断的子波-S型神经网络 |
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| 引用本文： | 赵劲松 陈丙珍. 一种用于动态过程故障诊断的子波-S型神经网络[J]. 化工学报, 1997, 48(1): 1-7 |
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| 作者姓名： | 赵劲松 陈丙珍 |
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| 作者单位： | 清华大学化学工程系,清华大学化学工程系,清华大学化学工程系 北京 100084,北京 100084,北京 100084 |
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| 基金项目： | 国家自然科学基金资助项目 |
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| 摘 要： | 在分析子波变换的基础上,将子波理论引入神经网络,提出了一种新的神经网络模型─—子波-S型神经网络(WSNN),并将其成功地应用于动态过程的故障诊断,同时根据其特性给出训练方法, 实验证明,与S形作用函数的前向阶层型神经网络(SBFN)相比,子波-S型神经网络提高了故障早期诊断的正确诊断率。
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| 关 键 词： | 故障诊断 子波变换 人工神经网络 动态过程 |
FAULT DIAGNOSIS IN DYNAMIC PROCESSES USING A NEW TYPE OF WAVELET - SIGMOIDAL FUNCTION BASED NEURAL NETWORKS |
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| Zhao Jinsong,Chen Bingzhen and Shen Jingzhu. FAULT DIAGNOSIS IN DYNAMIC PROCESSES USING A NEW TYPE OF WAVELET - SIGMOIDAL FUNCTION BASED NEURAL NETWORKS[J]. Journal of Chemical Industry and Engineering(China), 1997, 48(1): 1-7 |
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| Authors: | Zhao Jinsong Chen Bingzhen Shen Jingzhu |
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| Abstract: | For improving the correctness rate of dynamic fault diagnosis, a new artificial neuralnetwork——wavelet-sigmoidal function based neural network(WSNN) is proposed after introducing wavelet transforms, and is successfully applied to dynamic process fault diagnosis. The training algorithm is also presented based on its error surface properties. In this paper, WSNN is compared with the traditional sigmoidal function based neural network (SBFN) in terms of the correctness rate of fault diagnosis. |
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| Keywords: | fault diagnosis wavelet transform artificial neural networks dynamic process |
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