通过储库计算控制混乱系统的控制.
Zi-Fei Lin1,2, Yan-Ming Liang1,3, Jia-Li Zhao1,4
1School of Mathematics, Xi'an University of Finance and Economics, Xi'an 710100, People's Republic of China.
Chaos (Woodbury, N.Y.)
|December 11, 2023
概括
这项研究表明,水库计算有效地控制了非线性动态系统中的混乱,即使是随机噪声. 优化神经网络参数可以提高机器学习性能,以控制混乱.
科学领域:
- 非线性动力学是一种非线性动力学.
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 混沌是决定性和随机非线性系统中无处不在的动态特征.
- 控制混乱是各个科学领域面临的重大挑战.
- 循环神经网络为非线性动态问题提供快速准确的解决方案.
研究的目的:
- 在动态系统中使用储库计算来控制混乱.
- 评估该方法对随机噪声系统的适用性.
- 研究神经网络参数对性能的影响.
主要方法:
- 利用储库计算,一种机器学习技术,来实现混乱控制.
- 将一个控制项纳入储计算算法.
- 分析不同神经元数量和泄漏率的影响.
主要成果:
- 储库计算算法成功控制了动态系统中的混乱现象.
- 拟议的方法在随机噪声的存在下证明了稳定性.
- 通过调整参数,适当构建神经网络,提高性能.
结论:
- 储计算为非线性系统中的混乱控制提供了一种有效的方法.
- 该方法可以适应杂的环境,扩大了其适用性.
- 优化神经网络架构对于增强基于机器学习的混乱控制至关重要.
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