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在不了解机制的情况下,对零碎的非线性动态系统的识别
Bochen Wang1, Liang Wang1, Jiahui Peng1
1Department of Applied Probability and Statistics, School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710129, Peoples Republic of China.
这项研究引入了一种新的算法,用于从数据中分段识别非线性动态系统. 该方法准确地发现了运动方程,并且在没有先前的系统知识的情况下捕获了非平滑的特征.
科学领域:
- 动态系统分析 动态系统分析
- 非线性系统识别 非线性系统识别
- 数据驱动的建模数据驱动的建模
背景情况:
- 准确地模拟零碎的非线性动态系统对于理解复杂现象至关重要.
- 现有的方法往往需要先前了解系统结构或模型术语.
- 识别有不连续性的系统在数据分析中是一个重大挑战.
研究的目的:
- 开发一个数据驱动的算法,以提炼零碎的非线性动态系统,而无需事先的知识.
- 准确识别这些系统的运动方程和非平滑特性.
- 通过精确的建模,实现复杂系统的预测和详细分析.
主要方法:
- 利用富里埃数列分解属性对利曼整合的零碎非线性系统.
- 利用富里埃数列近似中的不准确性来检测零碎的特征和不连续性集.
- 识别每个段的动态系统作为纯佛里埃数列.
主要成果:
- 拟议的算法准确地发现了运动的基本方程.
- 该方法精确地捕获了系统的非平滑特性和不连续性集.
- 复杂的模型识别通过一系列简化步骤实现.
结论:
- 开发的算法提供了一种有效的方法,用于从数据中分段识别非线性动态系统.
- 这种方法消除了对先前知识的需求,简化了复杂系统的分析.
- 准确识别系统动态和非平滑特征有助于随后的预测和分析.
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