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Nonlinear Systems Identification via an Input-Output Model Based on a Feedforward Neural Network

Nonlinear Systems Identification via an Input Output Model Based on a Feedforward Neural Network
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摘要 This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model. This paper develops a feedforward neural network based input output model for a general unknown nonlinear dynamic system identification when only the inputs and outputs are accessible observations. In the developed model, the size of the input space is directly related to the system order. By monitoring the identification error characteristic curve, we are able to determine the system order and subsequently an appropriate network structure for systems identification. Simulation results are promising and show that generic nonlinear systems can be identified, different cases of the same system can also be discriminated by our model.
出处 《International Journal of Plant Engineering and Management》 1997年第4期45-50,共6页 国际设备工程与管理(英文版)
关键词 nonlinear dynamic systems identification neural networks based Input Output Model identification error characteristic curve nonlinear dynamic systems identification, neural networks based Input Output Model, identification error characteristic curve
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