利用经验模型还原和迁移学习改进 RBFNN 优化控制的鲁棒性

IF 1.6 4区计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS

International Journal of Control Pub Date : 2024-03-21 DOI:10.1080/00207179.2024.2328687

Anni Zhao, Arash Toudeshki, Reza Ehsani, Joshua H. Viers, Jian-Qiao Sun

引用次数: 0

摘要

本文提出了一种用带有高斯神经元的径向基函数神经网络（RBFNN）计算动态系统优化控制解的方法。RBFNN 用于计算动态系统的最优控制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Robustness improvement of optimal control in terms of RBFNN with empirical model reduction and transfer learning

This paper proposes a method to compute solutions of optimal controls for dynamic systems in terms of radial basis function neural networks (RBFNN) with Gaussian neurons. The RBFNN is used to compu...

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来源期刊

International Journal of Control 工程技术-自动化与控制系统

CiteScore

5.00

自引率

9.50%

发文量

197

审稿时长

5.3 months

期刊介绍： The International Journal of Control publishes top quality, peer reviewed papers in all areas, both established and emerging, of control theory and its applications. Readership: Development engineers and research workers in industrial automatic control. Research workers and students in automatic control and systems science in universities. Teachers of advanced automatic control in universities. Applied mathematicians and physicists working in automatic control and systems analysis. Development and research workers in fields where automatic control is widely applied: process industries, energy utility industries and advanced manufacturing, embedded systems and robotics.