适应性整体交替最小化方法用于从高度损坏的数据中对非线性动态系统进行强有力的学习.
Tao Zhang1,2, Guang Liu1,2, Li Wang1
1School of Aeronautics and Astronautics, Shenzhen Campus of Sun Yat-sen University, No. 66 Gongchang Road, Guangming District, Shenzhen, Guangdong 518107, People's Republic of China.
Chaos (Woodbury, N.Y.)
|December 11, 2023
概括
本研究介绍了一种自适应积分交替最小化方法 (AIAMM),用于从损坏的数据中学习非线性动态系统. AIAMM有效地识别系统动态,即使有大量的噪音和异常值,性能优于现有方法.
科学领域:
- 动态系统理论 动态系统理论
- 机器学习 机器学习
- 信号处理 信号处理
背景情况:
- 从噪音数据中学习非线性动态系统是具有挑战性的.
- 现有的方法与高度腐败的测量和异常值作斗争.
- 准确的系统识别对于理解和控制复杂现象至关重要.
研究的目的:
- 从高度损坏的数据中开发一种强大的学习非线性动态系统的方法.
- 为了应对未知的稀疏系数,初始值和异常值的挑战.
- 提高系统识别在存在大量噪声时的准确性和可靠性.
主要方法:
- 提出了一个自适应的整体交替最小化方法 (AIAMM).
- 以稀疏稳健线性回归来制定这个问题.
- 引入了适应性值参数选择,用于稀疏性控制.
主要成果:
- AIAMM成功地从高度损坏的数据中学习非线性动态系统.
- 在各种系统上表现出卓越的性能,如范德波尔振荡器和洛伦兹系统.
- 在稳定性和准确性方面超过了先进的稀疏回收方法.
结论:
- AIAMM是一种强大而准确的方法,用于从噪音数据中识别非线性动态系统.
- 适应性值有效地处理模型安装错误和稀疏性.
- AIAMM为有损测量的系统识别提供了重大进展.
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