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• W2891796227 abstract "This paper presents a method to identify a nonlinear system, employing high order neural networks. Given an unknown discrete-time nonlinear plant subject to disturbances, a high order neural network is proposed to approximate its dynamics. To train the neural network weights, considered as new states, the cubature Kalman filter is used. A simulation example and an experimental study are included to show effectiveness of the proposed scheme. The results given by the proposed method are compared to the results produced by the extended Kalman filter training algorithm." @default.
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• W2891796227 date "2018-12-01" @default.
• W2891796227 modified "2023-10-15" @default.
• W2891796227 title "Discrete-time high order neural network identifier trained with cubature Kalman filter" @default.
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• W2891796227 doi "https://doi.org/10.1016/j.neucom.2018.08.078" @default.
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