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一些对单调非线性方程的自缩放联梯度方法的全球融合,并应用于3DOF手臂机器人模型
Sulaiman M Ibrahim1,2, Lawal Muhammad3, Rabiu Bashir Yunus4,5
1Institute of Strategic Industrial Decision Modelling, School of Quantitative Sciences, Universiti Utara Malaysia, Sintok, Kedah, Malaysia.
PloS one
|January 24, 2025
概括
本研究引入了缩放的结合梯度 (CG) 参数,以确保非线性方程的全球收. 新的方法有效地解决复杂的问题,包括机器人操纵器反向动力学.
科学领域:
- 数字分析 数字分析
- 优化算法的优化算法
- 计算数学是指计算数学.
背景情况:
- 结合梯度 (CG) 方法对于大规模非线性系统是有效的.
- 对CG方法的全球收分析是一个重大挑战.
- 现有的方法往往需要精确的线路搜索,限制了适用性.
研究的目的:
- 为非线性方程提出新的缩放结合梯度 (CG) 参数.
- 确保全球收独立于线索搜索准确度.
- 提高CG方法的效率和适用性.
主要方法:
- 以巴西莱-博尔韦恩方法为灵感,开发规模化的CG参数.
- 实现凸约束单调非线性方程的算法.
- 确保足够的下降属性,以改善收.
主要成果:
- 拟议的规模化CG方法在特定假设下证明了全球趋同.
- 算法在没有精确的线索搜索的情况下实现了足够的下降属性.
- 数字实验证实了大型非线性系统的效率.
结论:
- 新型缩放的CG方法为非线性方程提供了可靠的解决方案.
- 这些方法对于复杂的应用,如机器人操纵器反向动力学,是有效的.
- 该研究推进了CG方法的融合分析和实际应用.
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