Complete controllability of continuous-time recurrent neural networks

Systems and Control Letters

Volumn 30, Issue 4, 1997, Pages 177-183

  Sontag, Eduardo ^a   Sussmann, Héctor ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

Global stabilization; Linear discrete time systems; Saturated feedback

Indexed keywords

DISCRETE TIME CONTROL SYSTEMS; FEEDBACK; MATRIX ALGEBRA; NEURAL NETWORKS; STABILITY; STABILIZATION;

GENERIC CONDITION;

CONTROLLABILITY;

EID: 0031146078     PISSN: 01676911     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-6911(97)00002-9     Document Type: Article

References (18)

1. F. Albertini, P. Dai Pra, Forward accessibility for recurrent neural networks, IEEE Trans. Automat. Control 40 (1995) 1962-1968.
2. F. Albertini, E.D. Sontag, For neural networks, function determines form, Neural Networks 6 (1993) 975-990.
3. F. Albertini, E.D. Sontag, State observability in recurrent neural networks, Systems & Control Lett. 22 (1994) 235-244.
4. F. Albertini, E.D. Sontag, V. Maillot, Uniqueness of weights for neural networks, in: R. Mammone (Ed.), Artificial Neural Networks for Speech and Vision, Chapman & Hall, London, 1993, pp. 115-125.
5. A.D. Back, A.C. Tsoi, FIR and IIR synapses, a new neural network, architecture for time-series modeling, Neural Computation 3 (1991) 375-385.
6. Y. Bengio, Neural Networks for Speech and Sequence Recognition, Thompson Computer Press, Boston, 1996.
7. B. Dasgupta, E.D. Sontag, Sample complexity for learning recurrent perceptron mappings, IEEE Trans. Inform. Theory 42 (1996) 1479-1487.
8. P. Koiran, E.D. Sontag, Vapnik-Chervonenkis dimension of recurrent neural networks, submitted.
9. R. Koplon, E.D. Sontag, Using Fourier-neural recurrent networks to fit sequential input/output data, Neurocomputing, to appear.
10. M. Matthews, A state-space approach to adaptive nonlinear filtering using recurrent neural networks, Proc. 1990 IASTED Symp. on Artificial Intelligence Applications and Neural Networks, Zurich, July 1990, pp. 197-200.
11. M.M. Polycarpou, P.A. Ioannou, Neural networks and on-line approximators for adaptive control, in: Proc. 7th Yale Workshop on Adaptive and Learning Systems, Yale University, 1992, pp. 93-798.
12. H.T. Siegelmann, E.D. Sontag, On the computational power of neural nets, J. Comp. System Sci. 50 (1995) 132-150.
13. E.D. Sontag, Mathematical Control Theory: Deterministic Finite Dimensional Systems, Springer, New York, 1990.
14. E.D. Sontag, Neural nets as systems models and controllers, in: Proc. 7th Yale Workshop on Adaptive and Learning Systems, Yale University, 1992, pp. 73-79.
15. E.D. Sontag, Neural networks for control, in: H.L. Trentelman and J.C. Willems (Eds.), Essays on Control: Perspectives in the Theory and its Applications, Birkhauser, Boston, 1993, pp. 339-380.
16. H.J. Sussmann, Uniqueness of the weights for minimal feedforward nets with a given input-output map, Neural Networks 5 (1992) 589-593.
17. R. Zbikowski, Lie algebra of recurrent neural networks and identifiability, in: Proc. Amer. Automat. Control Conf., San Francisco, 1993, pp. 2900-2901.
18. R. Zbikowski, K.J. Hunt (Eds.), Neural Adaptive Control Technology, World Scientific, Singapore, 1996.