TD-LSTM-S模型在二氧化碳浓度预测中的应用  

Application of TD-LSTM-S model to carbon dioxide concentration prediction

作　　者：付子骏 吴永明[1] 徐计 FU Zijun;WU Yongming;XU Ji(State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China;College of Computer Science and Technology,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学公共大数据国家重点实验室,贵阳550025 [2]贵州大学计算机科学与技术学院,贵阳550025

出　　处：《重庆理工大学学报（自然科学）》2023年第4期192-199,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(51505094,61962009);贵州省科学技术基金计划项目[(2016)1037],[ZK(2023)079];贵州省科技支撑计划项目[(2017)2029];贵州大学引进人才科研项目[贵大人基合字(2014)60号]。

摘　　要：针对传统预测模型无法利用多元数据变量间内在联系的问题,提出了基于张量分解和序列最小二乘规划(SLSQP)优化的长短期记忆神经网络(LSTM)模型TD-LSTM-S。在模型中将数据构建成张量并对其进行分解与优化,使数据能够保留变量间的内在联系,采用SLSQP算法进行优化,使LSTM能够有效利用变量间的内在联系,提高模型的预测性能。实验结果表明:提出的TD-LSTM-S模型较传统模型具有更高的预测性能。To address the problem that traditional prediction models cannot exploit the intrinsic connections among variables of multivariate data,this paper proposes a long and short-term memory(LSTM)neural network model,TD-LSTM-S,which is based on tensor decomposition and sequential least square quadratic programming(SLSQP)optimization.In the model,the data are constructed into tensors and are decomposed and optimized so that the data can retain the intrinsic connections among variables.The SLSQP algorithm is used to optimize the LSTM so that it can effectively use the intrinsic connections among variables to improve the prediction performance of the model.The experimental results show that the proposed TD-LSTM-S model has higher prediction performance than the traditional model.

关 键 词：二氧化碳浓度预测 多元数据变量间内在联系 张量分解 序列最小二乘规划 长短期记忆神经网络 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]