Approximation of compositional functions with ReLU neural networks-Reference-Cited by-同舟云学术

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Approximation of compositional functions with ReLU neural networks

Published:2023-05 Issue: Volume:175 Page:105508
ISSN:0167-6911
Container-title:Systems & Control Letters
language:en
Short-container-title:Systems & Control Letters

Author:

Gong Qi^ORCID,Kang Wei,Fahroo Fariba

Funder

U.S. Naval Research Laboratory

National Science Foundation

Air Force Office of Scientific Research

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,General Computer Science,Control and Systems Engineering

Reference43 articles.

1. Dynamic Programming, Rand Corporation Research Study;Bellman,1957

2. Overcoming the curse of dimensionality for some Hamilton–Jacobi partial differential equations via neural network architectures;Darbon;Res. Math. Sci.,2020

3. Learning dynamical systems from data: a simple cross-validation perspective, part I: Parametric kernel flows;Hamzi;Physica D,2021

4. Overcoming the curse of dimensionality for approximating Lyapunov functions with deep neural networks under a small-gain condition;Grüne;IFAC-PapersOnline,2021

5. Polynomial approximation of high-dimensional Hamilton-Jacobi-Bellman equations and applications to feedback control of semilinear parabolic PDEs;Kalise;SIAM J. Sci. Comput.,2018

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