例如,通过数据清洗、去噪、归一化等手段提高数据的质量;同时,我们还可以采用数据增强技术,如生成对抗网络(GAN)等,生成更多的训练样本,以提高模型的泛化能力。9.4融合其他预测方法虽然CNN-BILSTM模型在电力负荷预测中表现出较好的性能,但仍然存在一定局限性。因此,我们可以考虑将其他预测方法与CNN-BILSTM模型进行融合,以...
基于GAN-CNN-BiLSTM的工业循环水系统供水泵故障诊断方法本发明提供一种工业循环水系统供水泵故障诊断方法,包括以下步骤:首先,利用三轴加速度传感器采集与供水泵状态强相关的驱动端振动信号,将振动信号进行信号图像转换,实现一维振动时序信号的三通道二维图化;采用生成对抗网络对原始样本进行数据增强,扩充供水泵故障图像数据...
卷积神经网络(ConvolutionalNeuralNetworks,CNN)是一类包含卷积计算且具有深度结构的前馈神经网络(FeedforwardNeuralNetworks),是深度学习的代表算法之一。 循环神经网络(RecurrentNeuralNetwork,RNN)是一类以序列数据为输入,在序列的演进方向进行递归且所有节点(循环单元)按链式连接的递归神经网络。 生成对抗网络(GAN,GenerativeA...
从入门到进阶,一口气讲透CNN、RNN、GAN、GNN、DQN、Transformer、LSTM等八大深度学习神经网络算法!真的不要太爽! 33 0 00:36 App 【Transformer-BILSTM-SVM多变量回归预测】基于Transformer-双向长短期记忆神经网络-支持向量机多变量回归预测。(可做分类/回归/时 102 0 00:44 App 基于贝叶斯算法优化双向时间...
In summary, this paper not only demonstrates the potential of GAN for generating realistic medical time series data, but also the proposed method can be extended to simulate real ECG waveforms of other types of cardiovascular diseases. 展开 ...
GAN is naturally suitable for the prediction of long-time wind power time series. This paper focuses on the short-term wind power prediction. The data set studied is the measured data of the wind farm. However, due to the irregularity of the data, then considering the superiority of the ...
2Jiangxi Zhonggan Investment Survey and Design Limited Company, Nanchang, China. *email: qiutaorong@ncu.edu.cn Scientific Reports | (2024) 14:1676 | https://doi.org/10.1038/s41598-024-51936-5 1 Vol.:(0123456789) www.nature.com/scientificreports/ Yiting et al.12 applied LSTM to the ...
Comparative analyses with established methods like DCNN, ANN-FLI, LungNet22, and SDAE-GAN underscore the efficacy of the proposed model. The innovative integration of hybrid MRMR-BiLSTM-CNN architecture and the horse herd optimization algorithm significantly enhances accuracy and F1 score, even with ...
Firstly, GAN-Cross is used to expand minority class sample data, thereby alleviating the issues of imbalanced minority class about the original dataset. Then, the Transformer module is used to adjust the ML-CNN-BiLSTM model to enhance the feature encoding ability of the intrusion model. Finally...
The results show that the proposed algorithm has high accuracy and good robustness when the sample size is seriously unbalanced.doi:10.1007/s10586-020-03055-9Zi-xian LiuDe-gan ZhangGu-zhao LuoMing LianBing LiuCluster Computing