构建了基于CNN-LSTM-AM的大坝预测模型.应用该大坝预测模型在工程实例中与LSTM,CNN-LSTM,LSTM-AM模型的预测结果和残差进行对比分析,CNN-LSTM-AM模型的预测结果和拟合度均更优;并以均方误差,均方根误差,平均绝对误差及决定系数R2作为精度评定指标对比各模型间预测性能,结果表明引入注意力机制能够提升模型预测性能,证实...
3、一种基于idscnn-am-lstm的沙尘暴预测方法,包括如下步骤: 4、步骤1,获取沙尘暴数据并进行预处理,预处理的数据以3维张量特征图形式表达,特征图尺寸为(t,w,d),其中t是采样时刻,代表特征图高度;w是沙尘暴数据代表特征图宽度;d是地区个数,代表特征图深度,即通道数; 5、步骤2,利用卷积神经网络进行一次时空特征...
The closing price forecasted by CNN, support vector regression (SVR), long short-term memory (LSTM), CNN-LSTM, gated recurrent unit - LSTM (GRU-LSTM), CNN-LSTM-AM, and CNN-STLSTM-AM are compared and analyzed. The results of experiments reflect the CNN-STLSTM-AM has the highest ...
基于CNN-LSTM模型的低压配电台区线损率预测研究 低压配电台区线损率是衡量电网企业线损管理水平的重要指标之一.基于电量数据合理地预测台区线损率将有助于电网企业预先进行故障巡查,故障排除以减少电能损耗,其对于提... 陈众 - 《电力设备管理》 被引量: 0发表: 2023年 一种基于改进1D-CNN-LSTM故障诊断模型 本发明...
The root mean square error (RMSE) index based on CNN-LSTM-AM method is reduced by 14.6 % and 13.8 % respectively, and the score index is increased by 2.0 % and 2.4 % respectively. The results show that the proposed method has higher accuracy in bearing RUL predict...
首先,基于IDSCNN设计能够匹配风电场群时空维度变换的可分离卷积核尺寸,对数值天气预报数据,实测功率数据进行一次时空特征提取,以获取气象–功率时空特征.然后,结合AM强化一次时空特征长时间序列中局部重要信息的贡献程度,筛选出与未来预测功率密切相关的二次时空特征,以作为LSTM预测模型的输入时间序列.最后,建立包含改进的...
Therefore, to overcome these problems, this article introduces a lightweight CNN model built on prior work combined with the LSTM-AM framework to obtain accurate fault detection of FW-UAVs with low power consumption and fast computations. First, lightweight CNN architecture aims to minimize ...
电力无线接入网异常流量检测深度学习注意力机制为了减轻电力无线专网系统因网络业务增多而带来的网络攻击以及异常流量入侵的安全事故隐患,提出了一种基于注意力机制的卷积-长短期记忆网络(convolution-long short-term memory network based on attention mechanism,AMCNN-LSTM)模型.该模型为避免序列特征稀疏分布的问题,采用卷...
To cope with unpredictable temperature fluctuations and long delay times, we propose an enhanced Convolutional Bidirectional Long Short-Term Memory Neural Network (CNN-Bi-LSTM-AM) model for temperature field prediction. The model integrates CNN for spatial feature extraction, Bi-LSTM for capturing ...