所蔵: Comparison study of computational parameter values between LRN and NARX in identifying nonlinear systems

Comparison study of computational parameter values between LRN and NARX in identifying nonlinear systems

To determine the nonlinear autoregressive model with exogenous inputs (NARX) parameter values is not an easy task, even though NARX is reported to successfully identify nonlinear systems. Apart from the activation functions, number of layers, layer size, learning rate, and number of epochs, the numb...

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主要な著者:	Nordin F.H., Nagi F.H., Zainul Abidin A.A.
その他の著者:	25930510500
フォーマット:	論文
出版事項:	2023
主題:	Layer recurrent network Nonlinear autoregressive with exogenous inputs Nonlinear system identification Recurrent neural network Complex networks Identification (control systems) Network layers Polynomials Recurrent neural networks Activation functions Computational parameters Correlation coefficient Investigate and analyze Non-linear autoregressive with exogenous Nonlinear autoregressive model with exogenous input (NARX) Polynomial equation Recurrent networks Nonlinear systems
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Comparison study of computational parameter values between LRN and NARX in identifying nonlinear systems

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