无限维次数下一代水库计算计算无限维次数
Lyudmila Grigoryeva1, Hannah Lim Jing Ting2, Juan-Pablo Ortega2
1Universität Sankt Gallen, Mathematics and Statistics Division, CH-9000, Switzerland.
Physical review. E
|April 18, 2025
概括
下一代储计算 (NG-RC) 现在更加高效和适应性. 这种先进的方法,框架为内核回归,改善复杂系统的时空预测.
科学领域:
- 复杂系统科学 复杂系统科学
- 机器学习 机器学习
- 计算神经科学是一种神经科学.
背景情况:
- 下一代储计算 (NG-RC) 显示出对复杂系统的时空预测具有前景.
- 传统的NG-RC面临着大型功能空间和超参数调整的挑战.
研究的目的:
- 为了证明NG-RC可以有效地使用内核回归来实现.
- 引入具有无限共变量的扩展NG-RC方法,增强适应性.
- 为拟议的方法提供理论支持和经验验证.
主要方法:
- 编码NG-RC作为内核回归以提高计算效率.
- 开发一个对无限共变量的扩展,消除对过去滞后和多项式特征的限制.
- 利用内核的普遍性特性进行理论分析.
主要成果:
- 核心回归公式使NG-RC训练在大型特征空间中变得高效和可行.
- 扩展的方法论是对过去的滞后数和多项式共变量的不可知.
- 数字结果表明,与传统的NG-RC相比,预测性能优越.
结论:
- 拟议的基于内核的NG-RC在理论上提供了合理且在实践中优越的空间时空预测替代方案.
- 这种通用化增强了复杂系统的水库计算的适用性和性能.
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