于 基于 EEMD-LSTM-MLR 的大坝变形组合预测模型 摘要 随着大坝建设的不断推进,大坝的安全性问题也成为了一个热门话题。本研究提出了一种基于 EEMD-LSTM-MLR 的大坝变形组合预测模型。首先,应用 EEMD 对大坝变形数据进行预处理,提取出其主要的非周期性和周期性成分;然后,利用 LSTM 模型对每个成分进行预测,再通过MLR...
Research Article Paddy Yield Prediction in Tamilnadu Delta Region Using MLR-LSTM Model Sathya P & Gnanasekaran P Article: 2175113 | Received 03 Nov 2022, Accepted 27 Jan 2023, Published online: 10 Feb 2023 Cite this article https://doi.org/10.1080/08839514.2023.2175113 CrossMark Full...
In this paper, we propose hybrid architecture for paddy yield prediction, namely, MLR-LSTM, which combines Multiple Linear Regression and Long Short-Term Memory to utilize their complementary nature. The results are compared with traditional machine learning methods such as Support vector machine, ...
针对短期电力负荷预测精度不高的问题,提出集合变分模态分解(VMD),长短期记忆(LSTM)网络及多元线性回归(MLR)的VMD-LSTM-MLR预测方法.先使用VMD将电力负荷数据分解为特征,频率均不同的本征模态函数,然后用LSTM对高频部分进行预测,用MLR对低频部分进行预测,最后将LSTM与MLR得到的预测结果进行叠加,获得完整的预测结果.使用...
In this paper, we propose hybrid architecture for paddy yield prediction, namely, MLR-LSTM, which combines Multiple Linear Regression and Long Short-Term Memory to utilize their complementary nature. The results are compared with traditional machine learning methods such as Support vector machine, ...
针对短期电力负荷预测精度不高的问题,提出集合变分模态分解(VMD),长短期记忆(LSTM)网络及多元线性回归(MLR)的VMD-LSTM-MLR预测方法.先使用VMD将电力负荷数据分解为特征,频率均不同的本征模态函数,然后用LSTM对高频部分进行预测,用MLR对低频部分进行预测,最后将LSTM与MLR得到的预测结果进行叠加,获得完整的预测结果.使用...
本发明提出了一种基于CEEMDLSTMMLR的短期电力负荷预测方法,步骤1:获取电力负荷数据,并对所得到的数据集进行预处理;步骤2:将输入数据通过CEEMD分解为有限个IMF分量和一个残余分量,根据各分量波动周期长短合并重组为高频分量和低频分量;步骤3:对高频分量应用LSTM神经网络进行预测,并用贝叶斯算法对LSTM网络超参数寻优;步骤...
In view of this, this paper proposes a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA) for the analysis and identification of the chemical composition of ancient glass. We separate the sampling points of ancient glass into two systems: lead-barium glass ...
Therefore, in this paper, a prediction method based on the combination of multiple linear regression and Long-Short-Term Memory (MLR-LSTM) is proposed, which uses the incomplete traffic flow data in the past period of time of the target prediction section and the continuous and complete traffic...
LSTM本文主要对股票预测的方式进行改进,使预测结果更接近真实数据.以往的股票价格预测研究大多简单地将股票价格作为序列数据,通过模型进行训练预测,或者只是通过分析新闻文本,股民评论的情感倾向预测股票价格的涨跌,这都不能全面地对股票价格进行考量.本文通过参考影响股票实际价格的多种因素,对股票价格预测结果展开研究.本...