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- W2076802571 abstract "A neural network based state estimator for a general class of nonlinear dynamic system is proposed. The proposed state estimator uses cascading of a recurrent neural network structure (RNN) which learns the internal behavior of the dynamical system and a feedforward neural network (FFNN) which learns the measuring relations of the system from the input-output data through prediction error minimization. A dynamic learning algorithm for training the recurrent neural network has been developed. The proposed method has been evaluated with different applications." @default.
- W2076802571 created "2016-06-24" @default.
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- W2076802571 date "2006-01-01" @default.
- W2076802571 modified "2023-10-18" @default.
- W2076802571 title "Neural Network Based State Estimation of Dynamical Systems" @default.
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- W2076802571 doi "https://doi.org/10.1109/ijcnn.2006.246803" @default.
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