Statically Indeterminate Problem Solving
Multi-input and Multi-variable systems
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Ampere-Maxwell's Law: Problem-Solving
Linear time-invariant Systems
Time-Domain Interpretation of PD Control
您也可能阅读
通过共同作者、期刊和引用图与本文相关的文章。
Zhijun Zhang1, Xiangliang Sun2, Xingru Li2
1School of Automation Science and Engineering, South China University of Technology, China; Key Library of Autonomous Systems and Network Control, Ministry of Education, China; Jiangxi Thousand Talents Plan, Nanchang University, Nanchang, China; College of Computer Science and Engineering, Jishou University, Jishou, China; Guangdong Artificial Intelligence and Digital Economy Laboratory (Pazhou Lab), Guangzhou, China; Shaanxi Provincial Key Laboratory of Industrial Automation, School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong, China; School of Information Science and Engineering, Changsha Normal University, Changsha, China; School of Automation Science and Engineering, and also with the Institute of Artificial Intelligence and Automation, Guangdong University of Petrochemical Technology, Maoming, China; Key Laboratory of Large-Model Embodied-Intelligent Humanoid Robot (2024KSYS004), China.
一个自适应变量参数动态学习网络 (AVDLN) 能够有效地解决时间变化的凸二次编程 (TVCQP) 问题. 与现有方法相比,这种新型网络提供了更快的融合和更少的错误.
科学领域:
背景情况:
研究的目的:
主要方法:
主要成果:
结论: