Abstract
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.
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Chow, T.W., Shuai, O. Feedforward Neural Networks Based Input-Output Models for Railway Carriage System Identification. Neural Processing Letters 5, 57–67 (1997). https://doi.org/10.1023/A:1009666009129
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DOI: https://doi.org/10.1023/A:1009666009129