A Fast U-d Factorization-based Learning Algorithm with Applications to Nonlinear System Modeling and Identification
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Youmin Zhang | X. Rong Li | Youmin Zhang | X. Li | X. Rong | Li
[1] X. R. Li,et al. Hybrid training of RBF networks with application to nonlinear systems identification , 1996, Proceedings of 35th IEEE Conference on Decision and Control.
[2] Dragan Obradovic,et al. On-line training of recurrent neural networks with continuous topology adaptation , 1996, IEEE Trans. Neural Networks.
[3] Guanrong Chen,et al. Modified extended kalman filtering for supervised learning , 1993 .
[4] Hideaki Sakai,et al. A real-time learning algorithm for a multilayered neural network based on the extended Kalman filter , 1992, IEEE Trans. Signal Process..
[5] Sharad Singhal,et al. Training feed-forward networks with the extended Kalman algorithm , 1989, International Conference on Acoustics, Speech, and Signal Processing,.
[6] Youmin Zhang,et al. A fast and robust recursive prediction error learning algorithm for feedforward neural networks , 1996, Proceedings of 35th IEEE Conference on Decision and Control.
[7] Francesco Palmieri,et al. Optimal filtering algorithms for fast learning in feedforward neural networks , 1992, Neural Networks.
[8] Stephen A. Billings,et al. Properties of neural networks with applications to modelling non-linear dynamical systems , 1992 .
[9] Richard D. Braatz,et al. On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.
[10] G. Bierman. Factorization methods for discrete sequential estimation , 1977 .
[11] P. Kumar,et al. Theory and practice of recursive identification , 1985, IEEE Transactions on Automatic Control.
[12] Lee A. Feldkamp,et al. Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.