最后,基于Spearman相关性系数定性分析结果选取气象因素构成不同的输入数据集,搭建CNN-BiLSTM组合模型.并引入注意力(Attention)机制根据不同时刻数据的特征状态对风电功率预测值的影响程度,进行差异化的权重分配,提高信息处理的效率.利用麻雀搜索算法确定了BiLSTM层的最优超参数,通过在相同的预测模型上输入不同的数据集,...
1.一种基于CNN-AM-BiLSTM残差网络的臭氧浓度预测方法,其特征在于,包括以下步骤: 1)获取监测站的多维度历史监测数据,并对其进行数据清洗; 2)构建基于卷积神经网络的特征提取模型,并对清洗后的数据进行特征提取; 3)构建臭氧浓度预测模型,利用提取的特征进行臭氧浓度预测。
This study introduces a novel hybrid model named STL-CNN-BILSTM-AM. It combines the seasonal-trend decomposition method with LOESS (STL) to simplify learning tasks and increase prediction accuracy for complex, nonlinear time-series data. Convolutional neural networks (CNNs) extract features from ...
Compared with other methods, the CNN-BiLSTM-AM method is more suitable for the prediction of stock price and for providing a reliable way for investors' to make stock investment decisions. 展开 关键词:CNN BiLSTM AM Stock price prediction ...
The results show that: (1) Compared to traditional machine learning algorithms, the CNN-BiLSTM-AM model has the ability to automatically learn deeper nonlinear features of convective weather. As a result, it exhibits higher forecasting accuracy on the convective weather dataset. F...
the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM). Fi...
the processing of titanium alloys, this study proposes a hybrid deep neural network fault diagnosis model that integrates the triangulation topology aggregation optimizer (TTAO), convolutional neural network (CNN), bidirectional long short-term memory network (BiLSTM), and attention mechanism (AM). Fi...
传统机器学习和深度学习模型在处理情感分类任务时会忽略情感特征词的强度,情感语义关系单薄,造成情感分类的精准度不高.提出一种融合情感词典的改进型BiLSTM-CNN+Attention情感分类算法.首先,通过融合情感词典的特征提取方法优化特征词的权重;其次,利用卷积神经网络(convolutional neural network, CNN)提取局部特征,利用双向长...
Therefore, this paper proposes a CNN-BiLSTM-AM method to predict the stock closing price of the next day. This method is composed of convolutional neural networks (CNN), bi-directional long short-term Memory (BiLSTM), and attention mechanism (AM). CNN is used to extract the features of ...
This study introduces a novel hybrid model named STL-CNN-BILSTM-AM. It combines the seasonal-trend decomposition method with LOESS (STL) to simplify learning tasks and increase prediction accuracy for complex, nonlinear time-series data. Convolutional neural networks (CNNs) extract features from ...