Neural Hyper Flow: Parameterized prediction for control systems

Mateus Mussi Brugnolli

doi:10.1016/j.engappai.2025.110413

2025-07-01

PRE

Mateus Mussi Brugnolli *

Bruno Augusto Angélico

DOI：10.1016/j.engappai.2025.110413

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摘要

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神经网络算子已被用于替代可能计算成本高昂的微分方程求解算法。然而，这些算子的应用不适合作为控制回路设计的模型。主要原因在于大多数算子每次训练时都考虑特定的初始条件。具体而言，控制应用需要更新其初始条件并重新计算控制律。本文旨在提供一种神经算子的新颖描述，使其能够无缝地作为替代方法应用于不同初始条件。通过利用超网络和选择适当的参数，本研究可应用于未来的控制应用。

Keyword:

Operator

Neural Operator

Ordinary differential equation

Control design

Hypernetworks

期刊

Engineering Applications of Artificial Intelligence

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5.7K

被引数:

3.5W

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Univ Sao Paulo

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