地球流体动力学模型恢复的长短期记忆网络渐进优化方法

GaryYen; 栗波; 谢胜利

doi:10.12052/gdutxb.210109

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地球流体动力学模型恢复的长短期记忆网络渐进优化方法

更新时间：2026-05-27

- 地球流体动力学模型恢复的长短期记忆网络渐进优化方法
- An Evolutionary Optimization of LSTM for Model Recovery of Geophysical Fluid Dynamics
- 广东工业大学学报 2021年38卷第6期页码：1-8
- 作者机构：
  
  1. 美国俄克拉荷马州立大学电气与计算机工程学院，俄克拉荷马州静水,74078
  2. 广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  Gary Yen(1963–)，男，教授，博士，IEEE Fellow，主要研究方向为智能控制、计算智能、进化多目标优化、条件健康监测、信号处理及其工业/国防应用
  谢胜利(1956–)，男，教授，博士，IEEE Fellow，主要研究方向为自适应信号处理、无线通信与网络、物联网信息技术，E-mail：shlxie@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(U1911401)
- DOI：10.12052/gdutxb.210109
  中图分类号： TP391.4
- 网络首发：2021-11-10，
  
  纸质出版：2021
- 稿件说明：
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GaryYen, 栗波, 谢胜利. 地球流体动力学模型恢复的长短期记忆网络渐进优化方法[J]. 广东工业大学学报, 2021, 38(6): 1-8.

Gary Yen, Li Bo, Xie Sheng-li. An Evolutionary Optimization of LSTM for Model Recovery of Geophysical Fluid Dynamics[J]. 2021, 38(6): 1-8.
GaryYen, 栗波, 谢胜利. 地球流体动力学模型恢复的长短期记忆网络渐进优化方法[J]. 广东工业大学学报, 2021, 38(6): 1-8. DOI： 10.12052/gdutxb.210109.

Gary Yen, Li Bo, Xie Sheng-li. An Evolutionary Optimization of LSTM for Model Recovery of Geophysical Fluid Dynamics[J]. 2021, 38(6): 1-8. DOI： 10.12052/gdutxb.210109.

摘要

地球物理流体动力学的计算模型在数据同化和不确定性量化等任务中的计算代价非常大。有人提出了相应的替代模型以寻求减轻计算负担。研究人员已经开始应用人工智能和机器学习算法

特别是人工神经网络

针对地球物理流体建立数据驱动的替代模型。神经网络的性能在很大程度上取决于其网络结构设计和超参数的选择(调参)。一般情况下

这些神经网络通过手动调参

反复试错

从而最大限度地提高其计算性能。这通常要求对底层神经网络结构以及特定领域问题有专业知识积累和认知。这一局限性可以通过使用进化算法

自动设计和选择神经网络的最优超参数来解决。本文应用遗传算法进行了有效的长短期记忆(Long Short-Term Memory

LSTM)神经网络设计

建立了NOAA海表温度数据集的温度预测模型。

Abstract

The computational models for geophysical fluid dynamics are computationally enormously expensive to employ in tasks such as data assimilation and uncertainty quantification. Naturally

surrogate models seeking to alleviate the computational burden has been proposed. Researchers have started applying artificial intelligence and machine learning algorithms

particularly artificial neural networks

to build data-driven surrogate models for geophysical flows. The performance of the neural network highly relies upon their architecture design and selection of hyper-parameters. These neural networks are usually manually crafted through trial and error to maximize their performance. This often demands specialized knowledge of the underlying neural network as well as the domain problems of interest. This limitation can be addressed by using an evolutionary algorithm to automatically design and select optimal hyper-parameters of the neural network. In this study

the genetic algorithm is applied to effectively design the long short-term memory (LSTM) neural network to build the forecasting model of the temperature in the NOAA sea-surface temperature data set.

关键词

Keywords

references

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