库存计算会议科尔摩戈罗夫-阿诺德网络:预测高维混乱系统的预测
Gen Li1, Liang Huang1, Youming Lei1,2
1School of Mathematics and Statistics, Northwestern Polytechnical University, Xi'an 710072, China.
Chaos (Woodbury, N.Y.)
|October 15, 2025
概括
我们介绍了库存计算科尔摩戈罗夫-阿诺德网络,用于预测复杂的混乱时间序列. 这种新的方法提高了预测准确度和对噪声的稳定性.
科学领域:
- 复杂系统科学 复杂系统科学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 高维的混乱时间序列预测是一个重大挑战.
- 传统的方法往往难以应对这些系统的复杂性和动态.
研究的目的:
- 提出一种新的混合方法,即库存计算科尔摩戈罗夫-阿诺德网络 (RC-KANs),用于改进混乱时间序列预测.
- 提高混乱系统建模的预测性能和稳定性.
主要方法:
- 开发了一种新的方法,将水库计算 (RC) 与科尔摩戈罗夫-阿诺德网络 (KAN) 结合起来.
- 将RC的线性输出层替换为KANs.
- 实施了使用教师强制和自由运行模式的混合培训计划.
主要成果:
- 在多尺度洛伦兹-96系统和Kuramoto-Sivashinsky方程上表现出卓越的预测能力.
- 展示了对噪声干扰和超参数变化的强度.
- 对比实验证实了拟议的RC-KANs方法的有效性.
结论:
- 库存计算科尔莫戈罗夫-阿诺德网络为高维混乱时间序列预测提供了强大的方法.
- 与传统技术相比,该方法提供了更高的准确性和稳定性.
- 在模拟复杂的动态系统方面,RC-KANs代表了显著的进步.
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