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• W1913426757 abstract "A parametric recurrent neural network (RNN) model and an improved dynamic backpropagation method of its learning are applied for real-time identification and state estimation of nonlinear plants. This RNN architecture has been expanded in a multimodel sense to identification of complex nonlinear plants. The obtained parameters of the RNN model are used for an adaptive control system design. The paper suggests performing a trajectory tracking state-space control for both cases. The applicability of the proposed adaptive control schemes is confirmed by simulation results." @default.
• W1913426757 created "2016-06-24" @default.
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• W1913426757 date "2002-11-13" @default.
• W1913426757 modified "2023-09-24" @default.
• W1913426757 title "A multimodel recurrent neural network for systems identification and control" @default.
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• W1913426757 doi "https://doi.org/10.1109/ijcnn.2001.939547" @default.
• W1913426757 hasPublicationYear "2002" @default.
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