1.Matlab实现GWO-CNN-LSTM-selfAttention灰狼算法优化卷积长短期记忆神经网络融合自注意力机制多变量多步时间序列预测,灰狼算法优化学习率,卷积核大小,神经元个数,以最小MAPE为目标函数; 自注意力层 (Self-Attention):Self-Attention自注意力机制是一种用于模型关注输入序列中不同位置相关性的机制。它通过计算每个位置...
By considering the benefits and feasibility of integrating multiple models, a VMD-CNN-LSTM-Self-Attention interval prediction method was innovatively proposed and developed. Empirical research was conducted using data from natural gas field station outgoing loads. The primary model constructed is a deep...
基于融合纠偏机制的CNN-Bi-LSTM-Self-Attention城市天级需水量预测软件是由上海交通大学著作的软件著作,该软件著作登记号为:2020SR0608641,属于分类,想要查询更多关于基于融合纠偏机制的CNN-Bi-LSTM-Self-Attention城市天级需水量预测软件著作的著作权信息就到天眼查官网
Received: 11 August 2022 DOI: 10.1049/gtd2.12763 Revised: 2 December 2022 Accepted: 8 January 2023 ORIGINAL RESEARCH IET Generation, Transmission & Distribution A deep LSTM-CNN based on self-attention mechanism with input data reduction for short-term load forecasting Shiyan Yi1 Haichun Liu2 Tao...
什么是transformer | Transformer是一种深度学习模型架构,最初由Google的研究团队于2017年提出,用于解决自然语言处理(NLP)中的序列到序列(Seq2Seq)问题。Transformer模型的核心是self-attention机制,能够高效地学习输入序列中的长距离依赖关系。与传统的RNN和CNN不同,Transformer采用了一种基于注意力机制的方法来处理输入...
Macadam是一个以Tensorflow(Keras)和bert4keras为基础,专注于文本分类、序列标注和关系抽取的自然语言处理工具包。支持RANDOM、WORD2VEC、FASTTEXT、BERT、ALBERT、ROBERTA、NEZHA、XLNET、ELECTRA、GPT-2等EMBEDDING嵌入; 支持FineTune、FastText、TextCNN、CharCNN、
This model integrates a self-attention-based convolutional neural network (CNN) with long short-term memory (LSTM) components to accurately classify various heart diseases from the mean of customized ECG patterns. The experiment for real-time diagnosis has been validated on Raspberry Pi 4 to ensure...
Then, using the self-attention mechanism (SAM), the crucial input load information is emphasized in the forecasting process. The calculation example shows that the proposed algorithm outperforms LSTM, LSTM-based SAM, and CNN-GRU-based SAM by more than 10% in eight different buildings, ...
This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map...
To solve this problem, a novel denoising LIB RUL prediction model based on convolution neural network (CNN) and long short-term memory (LSTM) with self-attention, namely, DCLA, is proposed in this article. In this model, a specially designed denoising autoencoder (DAE) is used to remove ...