The results show that the performance of this method is the best, MAE and RMSE are the smallest (which are 21.952 and 31.694). R is the largest (its value is 0.9804). Compared with other methods, the CNN-BiLSTM-AM method is more suitable for the prediction of stock price and for ...
因此,在使用CNN-BiLSTM模型时,需要进行参数调整以获得最佳性能。 综上所述,基于融合正余弦和柯西变异的麻雀优化算法(SCSSA)-CNN-BiLSTM模型可以充分利用SCSSA算法的优化能力和CNN-BiLSTM模型的时间序列建模能力,提高时间序列预测的准确性。这种模型能够在时间序列数据中找到最优的参数组合,并利用CNN和BiLSTM来提取特征...
主要内容 该程序实现多输入单输出预测,通过融合正余弦和柯西变异改进麻雀搜索算法,对CNN-BiLSTM的学习率、正则化参数以及BiLSTM隐含层神经元个数等进行优化,并对比了该改进算法和粒子群、灰狼算法在优化方面的优势。该程序数据选用的是一段风速数据,数据较为简单,方便同学进行替换学习。程序对比了优化前和优化后的效果...
The forecasting model showed great performance for the market stock profit. Luo et al. [10] used LSTM to forecast future stock price. With the application and development of artificial intelligence, deep learning is increasingly used in exchange rate forecasting. Models CNN-STLSTM-AM CNN can ...
A novel framework using 3D-CNN and BiLSTM model with dynamic learning rate scheduler for visual speech recognition. SIViP 18, 5433–5448 (2024). https://doi.org/10.1007/s11760-024-03245-7 Download citation Received07 March 2024 Revised22 April 2024 Accepted26 April 2024 Published18 May 2024 ...
In particular, our designed method extracts label meaning, the CNN layer extracts local semantic features of the texts, the BiLSTM layer fuses the contextual features of the texts and the local semantic features, and the attention layer selects the most relevant features for each label. We ...
为了考虑提示令牌之间的交互,他们将提示嵌入表示为BiLSTM(Graves等,2013)的输出。P-tuning还引入了在模板内使用任务相关锚定令牌(例如关系提取中的“capital”)以进一步改进的方法。这些锚定令牌在训练期间不进行调整。Han等(2021)提出了带规则的提示调优(PTR),该方法使用手工制作的子模板使用逻辑规则组成完整的模板。
BiLSTM + CNN + CRF (our)NER-2003 shared task (English)90.59 STag_BLCC,Eger et. al., 2017AM Persuasive Essays, Paragraph Level64.74 +/- 1.97 BiLSTM + CNN + CRF (our)AM Persuasive Essays, Paragraph Level64.54 In order to ensure the consistency of the experiments, for evaluation purposes...
To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection, a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed. The first layer of the wide convolutional kernel deep ...
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