A learning result for continuous-time recurrent neural networks

DOI10.1016/S0167-6911(98)00006-1zbMath0909.93011OpenAlexW1978418345MaRDI QIDQ1274412

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Publication date: 12 January 1999

Published in: Systems \& Control Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-6911(98)00006-1

zbMATH Keywords

system identificationcomputational learning theoryrecurrent neural networksreliable generalization

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) System identification (93B30)

Related Items (1)

Pole assignment for stochastic systems with unknown coefficients

Cites Work

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• Estimation of dependences based on empirical data. Transl. from the Russian by Samuel Kotz
• Turing computability with neural nets
• Decision theoretic generalizations of the PAC model for neural net and other learning applications
• Vapnik-Chervonenkis dimension of recurrent neural networks
• State observability in recurrent neural networks
• Complete controllability of continuous-time recurrent neural networks
• Bounding the Vapnik-Chervonenkis dimension of concept classes parameterized by real numbers
• Sample complexity for learning recurrent perceptron mappings

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