Identification of nonlinear dynamics using a general spatio-temporal network
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[1] K. Narendra,et al. An iterative method for the identification of nonlinear systems using a Hammerstein model , 1966 .
[2] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[3] George M. Siouris,et al. Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.
[4] Stephen A. Billings,et al. Identi cation of nonlinear systems-A survey , 1980 .
[5] Stephen A. Billings,et al. Identification of systems containing linear dynamic and static nonlinear elements , 1982, Autom..
[6] Stephen Grossberg,et al. Absolute stability of global pattern formation and parallel memory storage by competitive neural networks , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[7] S. A. Billings,et al. Structure Detection and Model Validity Tests in the Identification of Nonlinear Systems , 1983 .
[8] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.
[9] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[10] Robert J. Marks,et al. An Artificial Neural Network for Spatio-Temporal Bipolar Patterns: Application to Phoneme Classification , 1987, NIPS.
[11] Fernando J. Pineda,et al. Generalization of Back propagation to Recurrent and Higher Order Neural Networks , 1987, NIPS.
[12] Fernando J. Pineda,et al. Dynamics and architecture for neural computation , 1988, J. Complex..
[13] Barak A. Pearlmutter. Learning state space trajectories in recurrent neural networks : a preliminary report. , 1988 .
[14] Barak A. Pearlmutter. Learning State Space Trajectories in Recurrent Neural Networks , 1989, Neural Computation.
[15] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[16] Fernando J. Pineda,et al. Time Dependent Adaptive Neural Networks , 1989, NIPS.
[17] Luis B. Almeida. Back propagation in non-feedforward networks , 1989 .
[18] Igor Aleksander,et al. Neural computing architectures: the design of brain-like machines , 1989 .
[19] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[20] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[21] Sheng Chen,et al. Representations of non-linear systems: the NARMAX model , 1989 .
[22] Sheng Chen,et al. Identification of MIMO non-linear systems using a forward-regression orthogonal estimator , 1989 .
[23] Kumpati S. Narendra,et al. Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.
[24] Jing Peng,et al. An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories , 1990, Neural Computation.
[25] Jacob Barhen,et al. Adjoint-Functions and Temporal Learning Algorithms in Neural Networks , 1990, NIPS.
[26] Stephen A. Billings,et al. Non-linear system identification using neural networks , 1990 .
[27] N. V. Bhat,et al. Use of neural nets for dynamic modeling and control of chemical process systems , 1990 .
[28] Sheng Chen,et al. Practical identification of NARMAX models using radial basis functions , 1990 .
[29] L. B. Almeida. A learning rule for asynchronous perceptrons with feedback in a combinatorial environment , 1990 .
[30] Sheng Chen,et al. Parallel recursive prediction error algorithm for training layered neural networks , 1990 .
[31] N. Z. Hakim,et al. A discrete-time neural network model for systems identification , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[32] Barak A. Pearlmutter. Dynamic recurrent neural networks , 1990 .
[33] Lennart Ljung,et al. Adaptation and tracking in system identification - A survey , 1990, Autom..
[34] Heinz Unbehauen,et al. Structure identification of nonlinear dynamic systems - A survey on input/output approaches , 1990, Autom..
[35] Eric A. Wan,et al. Temporal backpropagation for FIR neural networks , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[36] Kil To Chong,et al. Recurrent multilayer perceptron for nonlinear system identification , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[37] Donald F. Specht,et al. A general regression neural network , 1991, IEEE Trans. Neural Networks.
[38] Kumpati S. Narendra,et al. Gradient methods for the optimization of dynamical systems containing neural networks , 1991, IEEE Trans. Neural Networks.
[39] Ah Chung Tsoi,et al. FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling , 1991, Neural Computation.
[40] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..
[41] S Z Qin,et al. Comparison of four neural net learning methods for dynamic system identification , 1992, IEEE Trans. Neural Networks.
[42] Robert O. Shelton,et al. A space-time neural network , 1992, Int. J. Approx. Reason..