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• W2020195018 abstract "Hammerstein model has been popularly applied to identify the nonlinear systems. In this paper, a Hammerstein-type neural network (HTNN) is derived to formulate the well-known Hammerstein model. The HTNN consists of a nonlinear static gain in cascade with a linear dynamic part. First, the Lipschitz criterion for order determination is derived. Second, the backpropagation algorithm for updating the network weights is presented, and the stability analysis is also drawn. Finally, simulation results show that HTNN identification approach demonstrated identification performances." @default.
• W2020195018 created "2016-06-24" @default.
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• W2020195018 date "2014-01-01" @default.
• W2020195018 modified "2023-10-17" @default.
• W2020195018 title "Identification of Nonlinear Dynamic Systems Using Hammerstein-Type Neural Network" @default.
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• W2020195018 doi "https://doi.org/10.1155/2014/959507" @default.
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