温度-KAN:通过改进的科尔摩戈罗夫-阿诺德网络,通过数据驱动的时间温度预测
Yongxiang Lei1, Bin Deng2, Ziyang Wang3
1School of Automation, Central South University, 410083, Changsha, China.
ISA transactions
|July 18, 2025
概括
本研究介绍了Tem2-KAN,这是一种用于准确预测气候温度的深度学习模型. 它使用基于spline的函数进行可解释的,高精度的预测,优于使用更少参数的传统方法.
科学领域:
- 气候信息学 气候信息学
- 人工智能的人工智能
- 气象学 天气学
背景情况:
- 准确的温度预测对于天气信息学和气候监测至关重要.
- 传统模型往往缺乏解释性,并与复杂的非线性气候模式作斗争.
- 深度学习提供了潜力,但经常导致"黑子"模型.
研究的目的:
- 介绍Tem2-KAN,这是一种用于高精度气候温度预测的新型深度学习架构.
- 用spline参数化的单变函数取代传统的神经网络权重,以实现动态学习.
- 整合通用近似与物理上有意义的特征可视化,用于可解释的气候模型.
主要方法:
- 在Kolmogorov-Arnold表示定理的基础上开发了Tem2-KAN.
- 实施了一条管道,包括气象数据预处理和Tem2-KAN培训.
- 对现实世界的英国气候数据集进行了严格的评估和废除研究.
主要成果:
- 在温度预测方面,Tem2-KAN实现了最先进的预测准确度.
- 与现有方法相比,该模型使用较少的可训练参数证明了卓越的性能.
- 废弃性研究量化了诸如线顺序和网格分辨率之类的超参数的影响.
结论:
- Tem2-KAN 提供了气候信息学中的范式转变,提供了高预测性能和机械洞察力.
- 该框架的超参数复杂性降低提高了其适用于操作预测的适用性.
- 理论和实践演示证实了Tem2-KAN的通用近似能力,可解释性和预测性能.
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