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FIR and UR synapses, a new neural network architecture for time-series modeling
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A.D. Back, A.C. Tsoi, FIR and UR synapses, a new neural network architecture for time-series modeling, Neural Comput. 3 (1991) 375-385.
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Sample complexity for learning recurrent perceptron mappings
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B. Dasgupta, E.D. Sontag, Sample complexity for learning recurrent perceptron mappings, IEEE Trans. Inform. Theory 42 (1996) 1479-1487.
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IEEE Trans. Inform. Theory
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Dasgupta, B.1
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Bounding the Vapnik-Chervonenkis dimension of concept classes parametrized by real numbers
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P. Goldberg, M. Jerrum, Bounding the Vapnik-Chervonenkis dimension of concept classes parametrized by real numbers, Machine Learning 18 (1995) 131-148.
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M. Karpinski, A. Macintyre, Polynomial bounds for VC dimension of sigmoidal and general Pfaffian neural networks, J. Comput. System Sci., to appear. (Summary in: Polynomial bounds for VC dimension of sigmoidal neural networks, in: Proc. 27th ACM Symp. on Theory of Computing, 1995, pp. 200-208.)
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Neural networks for control
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H.L. Trentelman, J.C. Willems (Eds.), Birkhauser, Boston
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E.D. Sontag, Neural networks for control, in: H.L. Trentelman, J.C. Willems (Eds.), Essays on Control: Perspectives in the Theory and its Applications, Birkhauser, Boston, 1993, pp. 339-380.
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Sontag, E.D.1
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0009038890
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Recurrent neural networks: Some systems-theoretic aspects
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M. Karny, K. Warwick, V. Kurkova (Eds.), Springer, London, to appear
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E.D. Sontag, Recurrent neural networks: some systems-theoretic aspects, in: M. Karny, K. Warwick, V. Kurkova (Eds.), Dealing with Complexity: a Neural Network Approach, Springer, London, 1998, to appear.
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Complete controllability of continuous-time recurrent neural networks
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E.D. Sontag, H.J. Sussmann, Complete controllability of continuous-time recurrent neural networks, Systems Control Lett. 30 (4) (1997) 177-183.
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Lie algebra of recurrent neural networks and identifiability
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San Francisco
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