mlsanityData to 14 Oct 2026

Universal Time-Uniform Trajectory Approximation for Random Dynamical Systems with Recurrent Neural Networks

arXiv 2211.08018 ↗First posted 15 Nov 2022cs.NE cs.LG math.DScited by 1

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Abstract (arXiv)

The capability of recurrent neural networks to approximate trajectories of a random dynamical system, with random inputs, on non-compact domains, and over an indefinite or infinite time horizon is considered. The main result states that certain random trajectories over an infinite time horizon may be approximated to any desired accuracy, uniformly in time, by a certain class of deep recurrent neural networks, with simple feedback structures. The formulation here contrasts with related literature on this topic, much of which is restricted to compact state spaces and finite time intervals. The model conditions required here are natural, mild, and easy to test, and the proof is very simple.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 21 of its 73 references that are in the map. First posted on arXiv 15 Nov 2022.

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21 of 73 references.

References · 21

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9 more
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  1. 2024Stochastic Reservoir Computerscited by 4