准备训练数据:将时间序列数据转换为适合CNN-BiLSTM输入的格式。 SCSSA优化:调用SCSSA优化算法找到最佳参数。 构建CNN-BiLSTM模型:根据最佳参数构建CNN-BiLSTM模型。 训练模型:使用最佳参数训练CNN-BiLSTM模型。 预测:在训练集和测试集上进行预测。 反归一化:将预测结果反归一化。 计算评价指标:计算R²、MAE、RMSE...
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...
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,注释清晰...
This paper puts forward a hybrid model CNN-STLSTM-AM to forecast the closing price of the USD/RMB exchange rate. Convolution neural network (CNN) extracts local characters of the input data. Special tanh long short-term memory (STLSTM), an improved model proposed in the paper, forecasts ...
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 ...
We propose a CNN-BiLSTM-Attention classifier to classify online short messages in Chinese posted by users on government web portals, so that a message can be directed to one or more government offices. Our model leverages every bit of information to carry out multi-label classification, to make...
targeris a PyTorch implementation of "mainstream" BiLSTM-CNN-CRF neural tagging method based on works ofLample, et. al., 2016andMa et. al., 2016. Requirements Python 3.5.2 or higher NumPy 1.15.1 SciPy 1.1.0 PyTorch >= 0.4.1
基于这些影响,本文在 Faster R-CNN 的共享网络 ResNet-50 中引入了深度残差收缩网络,此网络利用注意力机制实现与当前任务目标有关的重要特征提取,抑制无关因素干扰。 [179] 任燕, 张瑞, 汤何胜等. 基于多模态深度残差收缩网络的液压防水阀故障诊断方法[P]. 浙江省:CN114358189A, 2022-04-15. ...
select article Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression Research articleFull text access Automatic cough detection from realistic audio recordings using C-BiLSTM with boundary regression Mingyu You, Weihao Wang, You Li, Jiaming Liu, ... Zhongmin...
et al. is a combination of CNN, BiLSTM, and attention, so it is difficult to separate it into a specific deep learning category [40]. The lack of a very clear standard in the process of classifying deep learning methods also increases the error of statistical analysis to a certain extent...