Approximation of state-space trajectories by locally recurrent globally feed-forward neural networks

DOI10.1016/J.NEUNET.2007.10.004zbMath1254.93107DBLPjournals/nn/Patan08OpenAlexW2018698390WikidataQ51897877 ScholiaQ51897877MaRDI QIDQ1931977

Krzysztof Patan

Publication date: 17 January 2013

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2007.10.004

zbMATH Keywords

universal approximation theoremstate-space representationLipschitz mappingrecurrent networksapproximation abilitycascade network

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) Discrete-time control/observation systems (93C55) Dynamical systems in control (37N35)

Related Items (2)

Local stability conditions for discrete-time cascade locally recurrent neural networks ⋮ Towards Robustness in Neural Network Based Fault Diagnosis

Cites Work

• Multilayer feedforward networks are universal approximators
• Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks
• Approximation by superpositions of a sigmoidal function
• Learning of Chua's circuit attractors by locally recurrent neural networks
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