A learning result for continuous - time recurrent neural networks 1

Abstract

The following learning problem is considered, for continuous-time recurrent neural networks having sigmoidal activation functions. Given a “black box” representing an unknown system, measurements of output derivatives are collected, for a set of randomly generated inputs, and a network is used to approximate the observed behavior. It is shown that the… (More)

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Cite this paper

@inproceedings{Sontag2014ALR, title={A learning result for continuous - time recurrent neural networks 1}, author={Eduardo D. Sontag}, year={2014} }