Feedforward Neural Networks Based Input-Output Models for Railway Carriage System Identification

被引：4

作者：

Tommy W.S. Chow

Oulian Shuai

机构：

[1] City University of Hong Kong,Department of Electronic Engineering

来源：

Neural Processing Letters | 1997年 / 5卷 / 2期

关键词：

identification error characteristic curve; neural networks input-output model; railway carriage vibration signal;

D O I：

10.1023/A:1009666009129

中图分类号：

学科分类号：

摘要：

Depending on the representation ability of neural networks for a nonlinear process, this paper develops procedures to locate the input space dimensions of feedforward neural networks for general practical nonlinear systems identification when only the outputs are accessible observations. The size of the input space is directly related to the involved system order. Hence based on our developed model, we are able to determine whether the system is faulty or not by monitoring the output error change. The methods are demonstrated in vibration signals that were measured by an accelerometer mounted on a traction centre of a railway carriage running at about 60 mph along the Hong Kong railway.

引用

页码：57 / 67

页数：10

共 35 条

[1] Isermann R.(1984)Process fault detection based on modelling and estimation methods – a survey Automatica 20 387-404
[2] Narendra K.S.(1990)Identification and control of dynamical systems using neural networks IEEE Transactions on Neural Networks 1 4-27
[3] Parthasarthy K.(1991)Neural networks in process fault diagnosis IEEE Transactions on Systems, Man & Cybernetics 21 815-825
[4] Sorsa T.(1990)Identification and control of dynamical systems using neural networks IEEE Transactions on Neural Networks 1 4-27
[5] Koivo H.N.(1991)Neural Networks in Process Fault Diagnosis IEEE Transactions on Systems, Man & Cybernetics 21 815-825
[6] Koivisto H.(1990)Use of neural networks for failure detection in a control system IEEE Control System Magazine 10 49-55
[7] Narendra K.S.(1991)Artificial neural networks in process engineering IEE Proc.-D 138 256-266
[8] Parthasarthy K.(1991)Fault diagnoses in complex chemical plants using artificial neural networks AIChE Journal 37 137-141
[9] Sorsa T.(1994)Fault diagnosis in complex systems using artificial neural networks Proceedings of the Third IEEE Conference on Control Applications 2 877-882
[10] Koivisto H.(1991)Arbitrary nonlinearity is sufficient to represent all functions by neural networks: A Theorem Neural Networks 4 381-383

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