Research on LSTM Water Level Prediction Model Based on Improved Attention Mechanism Ma Fei 1, Tu Zhenyu 1*, Zhu Songting 2, Xiang Xinyue 1, Sun Yifei 1, Fang Qiang 1 (1. School of Information Engineering, Nanchang Engineering College, Nanchang, Jiangxi, 330099; 2. Jiangxi Flood Control Information Center, Nanchang, Jiangxi, 330009) Abstract In order to further … Read more
♚ Author: Yishui Hancheng, CSDN Blog Expert, Research Directions: Machine Learning, Deep Learning, NLP, CV Blog: http://yishuihancheng.blog.csdn.net Traditional linear models struggle with multivariate or multi-input problems, whereas neural networks like LSTM excel at handling multiple variables, making them suitable for time series prediction tasks. In the following article, you will learn how to build an … Read more
Source: DeepHub IMBA This article is approximately 5500 words, recommended reading time is over 10 minutes. This article explores the hybrid modeling method that combines temporal features and static features in the field of quantitative trading. By integrating Stacked Sparse Denoising Autoencoder (SSDA) and Long Short-Term Memory based Autoencoder (LSTM-AE), we aim to build a … Read more
Source: DeepHub IMBA This article is about 1300 words long, and it is recommended to read it in 5 minutes. This paper treats heart sound signals as speech signal processing and achieves good results. This is a very interesting paper that proposes two heart rate sound classification models based on the log-mel spectrogram of heart … Read more
Click the blue text| Follow “Electrical Engineering” Abstract:Traditional neural networks have low accuracy in load forecasting with strong temporal correlation. To effectively improve the accuracy of short-term power load forecasting, a load forecasting method based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network is proposed. Five-dimensional load feature data … Read more
Research on LSTM Model for River Dissolved Oxygen Prediction Optimized by Attention Mechanism Zhou Quan1, Hu Xuanming2, Wang Dongkun2, Zhang Wucai1, Chen Zhongying1, Wang Jinpeng1, Wang Pengyang2, Ren Xiuwen1 1. South China Institute of Environmental Sciences, Ministry of Ecology and Environment, Key Laboratory of Water Environment Simulation and Pollution Control, Guangzhou, Guangdong 5105302. University of … Read more
Click the “MLNLP” above, select to “star” or “pin” Important content delivered promptly Editor: Yi Zhen https://www.zhihu.com/question/62399257 This article is for academic sharing only. If there is infringement, it will be deleted. Understanding LSTM Followed by CRF Author:Scofieldhttps://www.zhihu.com/question/62399257/answer/241969722 In short, I will write a detailed article when I have time. 1. Perspective Everyone knows that … Read more
Cite this article REN Hongbo, WU Qiong, WANG Xiangyu, et al. Uncertainty Plan of a Zero-carbon Energy System Based on LSTM Quantile Regression[J]. Journal of Shanghai University of Electric Power, 2023, 39(2):149-157. Abstract The zero-carbon energy system is an effective way to promote the construction of a clean and low-carbon energy system and achieve carbon … Read more
Reasons for Recommending Quality Articles To address the issue of predicting the state of charge (SOC) of lithium-ion batteries, a predictive model based on Long Short-Term Memory (LSTM) Recurrent Neural Networks is constructed. The test results show that incorporating the Dropout regularization method during the model training process can effectively reduce overfitting and enhance the … Read more
1. Task Introduction Mainly to train a stock selection model using 2000 stock selection factors as input features, with returns as labels, to train the LSTM model to predict stocks with higher returns on a future trading day. This article mainly introduces the data processing and model building parts, and analyzes different standardization techniques in … Read more