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Load-measurement method for floating offshore wind turbines based on a long short-term memory (LSTM) neural network 认领 引用 被引量：2
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作者 Yonggang LIN Xiangheng FENG +1 位作者 Hongwei LIU Yong SUN 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2025年第5期456-470,共15页
Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,w... Complicated loads encountered by floating offshore wind turbines(FOWTs)in real sea conditions are crucial for future optimization of design,but obtaining data on them directly poses a challenge.To address this issue,we applied machine learning techniques to obtain hydrodynamic and aerodynamic loads of FOWTs by measuring platform motion responses and wave-elevation sequences.First,a computational fluid dynamics(CFD)simulation model of the floating platform was established based on the dynamic fluid body interaction technique and overset grid technology.Then,a long short-term memory(LSTM)neural network model was constructed and trained to learn the nonlinear relationship between the waves,platform-motion inputs,and hydrodynamic-load outputs.The optimal model was determined after analyzing the sensitivity of parameters such as sample characteristics,network layers,and neuron numbers.Subsequently,the effectiveness of the hydrodynamic load model was validated under different simulation conditions,and the aerodynamic load calculation was completed based on the D'Alembert principle.Finally,we built a hybrid-scale FOWT model,based on the software in the loop strategy,in which the wind turbine was replaced by an actuation system.Model tests were carried out in a wave basin and the results demonstrated that the root mean square errors of the hydrodynamic and aerodynamic load measurements were 4.20%and 10.68%,respectively. 展开更多
关键词 Floating offshore wind turbine(FOWT) Long short-term memory(LSTM)neural network Machine learning technique Load measurement Hybrid-scale model test
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Dynamic prediction of landslide displacement using singular spectrum analysis and stack long short-term memory network 认领 引用 被引量：4
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作者 LI Li-min Zhang Ming-yue WEN Zong-zhou 《Journal of Mountain Science》 SCIE CSCD 2021年第10期2597-2611,共15页
An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models... An accurate landslide displacement prediction is an important part of landslide warning system. Aiming at the dynamic characteristics of landslide evolution and the shortcomings of traditional static prediction models, this paper proposes a dynamic prediction model of landslide displacement based on singular spectrum analysis(SSA) and stack long short-term memory(SLSTM) network. The SSA is used to decompose the landslide accumulated displacement time series data into trend term and periodic term displacement subsequences. A cubic polynomial function is used to predict the trend term displacement subsequence, and the SLSTM neural network is used to predict the periodic term displacement subsequence. At the same time, the Bayesian optimization algorithm is used to determine that the SLSTM network input sequence length is 12 and the number of hidden layer nodes is 18. The SLSTM network is updated by adding predicted values to the training set to achieve dynamic displacement prediction. Finally, the accumulated landslide displacement is obtained by superimposing the predicted value of each displacement subsequence. The proposed model was verified on the Xintan landslide in Hubei Province, China. The results show that when predicting the displacement of the periodic term, the SLSTM network has higher prediction accuracy than the support vector machine(SVM) and auto regressive integrated moving average(ARIMA). The mean relative error(MRE) is reduced by 4.099% and 3.548% respectively, while the root mean square error(RMSE) is reduced by 5.830 mm and 3.854 mm respectively. It is concluded that the SLSTM network model can better simulate the dynamic characteristics of landslides. 展开更多
关键词 Landslide Singular spectrum analysis Stack long short-term memory network Dynamic displacement prediction
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Recognition and classification of microseismic signals based on Bayesian-optimized CNN-LSTM neural network 认领 引用
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作者 WU Yang LIU Jian-feng +4 位作者 WANG Chun-ping LIU Jun-jie JI Zhen-xing TIAN Cheng-yu XUE Fu-jun 《Journal of Central South University》 SCIE EI CAS CSCD 2026年第6期2762-2787,共26页
Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"S... Microseismic monitoring and signal recognition constitute critical technologies for accurately assessing rockburst risks and ensuring the safe construction of underground rock engineering.This study developed a"Surface+Underground"microseismic intelligent monitoring system to evaluate dynamic disaster risks during construction at the Beishan High-level Radioactive Waste Geological Disposal Laboratory in China.The datasets of four typical one-dimensional time-domain microseismic signals,including rock fracture,blasting,TBM tunneling,and drilling,were constructed,and the BO-CNN-LSTM model is developed to identify and classify these signals..Based on the classification results,the typical time-frequency domain characteristics of the four types of signals are analyzed.The classification results of BO-CNN-LSTM,CNN,LSTM and CNN-LSTM models show that the recognition accuracy of the four models is 98.0%,85.7%,66.7%and 91.0%,respectively.Among all types,the four models demonstrate the highest effectiveness in identifying rock fracture and borehole signals.The findings further confirm that the BO-CNN-LSTM model efficiently recognizes and extracts microseismic signal features,demonstrating superior performance and stability in the classification task.Finally,the study suggests several future research directions,particularly in the areas of raw signal denoising,and automation of feature extraction. 展开更多
关键词 deep ground engineering microseismic monitoring signal classification neural network Bayesian optimization convolutional neural network(CNN) long short-term memory network(LSTM) BO-CNN-LSTM model
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Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed 认领 引用 被引量：2
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作者 Neelam Mughees Mujtaba Hussain Jaffery +2 位作者 Abdullah Mughees Anam Mughees Krzysztof Ejsmont 《Computers, Materials & Continua》 SCIE EI 2023年第6期6375-6393,共19页
Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely h... Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by 2050.However,they are exceedingly unpredictable since they rely highly on weather and atmospheric conditions.In microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of microgrids.With this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in MATLAB.To create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the forecasts.Hyperparameters of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization algorithm.Moreover,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE model.All these models were also optimized and modeled in MATLAB.The results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting. 展开更多
关键词 Deep stacked autoencoder sequence to sequence autoencoder bidirectional long short-term memory network wind speed forecasting solar irradiation forecasting
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ConvNeXt网络及Stacked BiLSTM-Self-Attention在轴承剩余寿命预测中的应用 认领 引用 被引量：2
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作者 张印文 王琳霖 +1 位作者 薛文科 梁文婕 《机电工程》 CAS 北大核心 2024年第11期1977-1985,1994,共9页
在滚动轴承剩余使用寿命预测方面,采用传统方法时存在鲁棒性差、精度低等各种问题。近些年来深度学习的发展为解决这些问题提供了新的思路。为了进一步提高对轴承寿命的预测精度,提出了一种基于ConvNeXt网络、堆叠双向长短时记忆网络(SB... 在滚动轴承剩余使用寿命预测方面,采用传统方法时存在鲁棒性差、精度低等各种问题。近些年来深度学习的发展为解决这些问题提供了新的思路。为了进一步提高对轴承寿命的预测精度,提出了一种基于ConvNeXt网络、堆叠双向长短时记忆网络(SBiLSTM)和自注意力机制(Self-Attention)的滚动轴承寿命预测方法。首先,采用连续小波变换(CWT)构造了振动信号的时频图,以更好地捕捉信号的时域和频域特征;然后,将得到的时频图输入到构建的ConvNeXt网络中,通过卷积、池化和层归一化等操作,对时频图的关键特征进行了提取;最后,将提取后的特征输入到SBiLSTM-Self-Attention模块中,进一步提取了时序信息和特征权重分配数据,利用PHM2012挑战数据集进行了验证,通过实验分析了该方法的均方根误差(RMSE)和平均绝对误差(MAE)。研究结果表明:相较于现有技术方法,该方法的平均RMSE为0.031;与其他三种方法,即卷积神经网络(CNN)、深度残差双向门控循环单元(DRN-BiGRU)和深度卷积自注意力双向门控循环单元(DCNN-Self-Attention-BiGRU)相比,其平均RMSE值分别下降了79%、74%和55%,MAE值分别下降了78%、73%和53%,说明该方法在滚动轴承剩余寿命预测中有较好的性能。 展开更多
关键词 滚动轴承 剩余寿命预测 ConvNeXt网络 堆叠双向长短时记忆网络 自注意力机制 深度学习 连续小波变换
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基于Stacking融合的LSTM-SA-RBF短期负荷预测 认领 引用 被引量：2
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作者 方娜 邓心 肖威 《重庆理工大学学报（自然科学）》 CAS 北大核心 2024年第4期131-137,共7页
为了解决单个神经网络预测的局限性和时间序列的波动性,提出了一种奇异谱分析(singular spectrum analysis,SSA)和Stacking框架相结合的短期负荷预测方法。利用随机森林筛选出与历史负荷相关性强烈的特征因素,采用SSA为负荷数据降噪,简... 为了解决单个神经网络预测的局限性和时间序列的波动性,提出了一种奇异谱分析(singular spectrum analysis,SSA)和Stacking框架相结合的短期负荷预测方法。利用随机森林筛选出与历史负荷相关性强烈的特征因素,采用SSA为负荷数据降噪,简化模型计算过程;基于Stacking框架,结合长短期记忆(long and short-term memory,LSTM)-自注意力机制(self-attention mechanism,SA)、径向基(radial base functions,RBF)神经网络和线性回归方法集成新的组合模型,同时利用交叉验证方法避免模型过拟合;选取PJM和澳大利亚电力负荷数据集进行验证。仿真结果表明,与其他模型比较,所提模型预测精度高。 展开更多
关键词 奇异谱分析 stacking算法 长短期记忆网络 径向基神经网络 短期负荷预测
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Three-degree-of-freedom motion posture stabilization control of platform based on DTW-LSTM-MATD3 under high and low frequency disturbances of ships 认领 引用
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作者 Qin ZHANG Jingyi ZHOU +1 位作者 Bangping GU Xiong HU 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2026年第3期246-261,共16页
In the complex and variable deep-sea environment,the compensation control of ship motion ensures the safety and efficiency of equipment installation and transportation in offshore wind farms.However,the ship motion po... In the complex and variable deep-sea environment,the compensation control of ship motion ensures the safety and efficiency of equipment installation and transportation in offshore wind farms.However,the ship motion posture compensation control system is severely affected by uncertainties,which significantly impact the accuracy of compensation control.In this paper,we propose a ship three-degree-of-freedom(3-DoF)motion posture stabilization control method based on the DTW-LSTM-MATD3 algorithm.We use the multi-agent twin delayed deep deterministic policy gradient(MATD3)to control a platform with six electric cylinders to achieve stable control.However,owing to random noise affecting the ship’s motion posture,we use a dynamic time warping(DTW)algorithm to distinguish between high-frequency noise and low-frequency tracking signals.Further,we embed a long short-term memory(LSTM)network into the MATD3 network to better align the Critic network’s training with the true Q-value.We use a combined reward function to enhance the agent’s exploration capability in complex dynamic environments.Finally,verification was conducted under sixth-level,abrupt sea conditions with high-frequency noise,as well as under real abrupt sea conditions,and a generalization test was also carried out.Simulation results show that the proposed DTW-LSTM-MATD3 method has great compensation control ability. 展开更多
关键词 Compensation control Multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm Dynamic time warping(DTW)algorithm Long short-term memory(LSTM)network
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Real-Time Prediction of Elbow Motion Through sEMG-Based Hybrid BP-LSTM Network 认领 引用
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作者 MA Yiyuan CHEN Huaiyuan CHEN Weidong 《Journal of Shanghai Jiaotong university(Science)》 EI 2025年第3期452-460,共9页
In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention i... In the face of the large number of people with motor function disabilities,rehabilitation robots have attracted more and more attention.In order to promote the active participation of the user's motion intention in the assisted rehabilitation process of the robots,it is crucial to establish the human motion prediction model.In this paper,a hybrid prediction model built on long short-term memory(LSTM)neural network using surface electromyography(sEMG)is applied to predict the elbow motion of the users in advance.This model includes two sub-models:a back-propagation neural network and an LSTM network.The former extracts a preliminary prediction of the elbow motion,and the latter corrects this prediction to increase accuracy.The proposed model takes time series data as input,which includes the sEMG signals measured by electrodes and the continuous angles from inertial measurement units.The offline and online tests were carried out to verify the established hybrid model.Finally,average root mean square errors of 3.52°and 4.18°were reached respectively for offline and online tests,and the correlation coefficients for both were above 0.98. 展开更多
关键词 motion prediction surface electromyography(sEMG) long short-term memory(LSTM) back-propagation neural network
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融合SKNet与堆叠LSTM的MobileNetV3齿轮箱故障识别方法 认领 引用
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作者 杨辰峰 杨喜旺 +2 位作者 黄晋英 范振芳 刘晶晶 《计算机系统应用》 2026年第1期237-245,共9页
当前基于深度学习的故障识别方法普遍面临高数据依赖性、高昂计算成本与时间开销,以及模型泛化能力受限等挑战.为此,本研究提出一种融合MobileNetV3、选择性核网络(selective kernel network,SKNet)及堆叠长短期记忆网络(stacked long s... 当前基于深度学习的故障识别方法普遍面临高数据依赖性、高昂计算成本与时间开销,以及模型泛化能力受限等挑战.为此,本研究提出一种融合MobileNetV3、选择性核网络(selective kernel network,SKNet)及堆叠长短期记忆网络(stacked long short-term memory network,Stacked LSTM)的轻量化高精度故障识别模型.首先进行输入数据预处理,将处理后的数据转换成适应卷积层的输入格式.在特征提取阶段,利用改进的MobileNetV3骨干网络进行深度特征挖掘,其倒置残差模块在保留深度可分离卷积高效性的基础上,策略性地嵌入SE(squeeze-andexcitation)与SK(selective kernel)双重注意力机制,有效兼顾通道信息交互与多尺度特征自适应选择,显著提升了特征表征能力并降低了计算复杂度.随后,堆叠LSTM捕获振动信号中的长距离时序依赖关系.最终通过全连接层实现特征压缩与分类决策,构建端到端识别系统.实验结果显示,本文模型识别准确率达到99.47%,与传统的齿轮箱故障识别技术相比,该方法在识别精准度和模型泛化能力方面均呈现出显著优势. 展开更多
关键词 故障识别 深度学习 MobileNetV3 选择性核网络 堆叠长短期记忆网络
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SINS/RCNS integrated navigation method based on LSTM algorithm for aerospace vehicle 认领 引用
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作者 YANG Shuning WANG Dingjie +1 位作者 ZHANG Hongbo TANG Guojian 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1030-1041,共12页
We propose a deep-learning-assisted strapdown inertial navigation system(SINS)efraction celestial navigation system(RCNS)integrated navigation method to control the adverse effects of atmospheric density errors on the... We propose a deep-learning-assisted strapdown inertial navigation system(SINS)efraction celestial navigation system(RCNS)integrated navigation method to control the adverse effects of atmospheric density errors on the accuracy of stellar refraction navigation and enhance the reliability of SINS/RCNS integrated navigation for aerospace vehicles.This method utilizes satellite navigation data and a long short-term memory network to establish a mapping relationship between the navigation moments,refraction angles,and the apparent height errors.Using deep learning algorithm to address complex time-series prediction problems,thereby compensates the impact of atmospheric density deviations on star sensor measurements.Simulation experiments of vehicle navigation in scenarios with atmospheric density errors are conducted using this method.The results show that the deep learning scheme can effectively resist the adverse effects of atmospheric density errors on navigation,demonstrating strong reliability. 展开更多
关键词 deep learning long short-term memory(LSTM)network stellar refraction navigation atmospheric density error inertial navigation
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State of Health Estimation of Lithium-Ion Batteries Using Support Vector Regression and Long Short-Term Memory 认领 引用 被引量：1
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作者 Inioluwa Obisakin Chikodinaka Vanessa Ekeanyanwu 《Open Journal of Applied Sciences》 2022年第8期1366-1382,共17页
Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate e... Lithium-ion batteries are the most widely accepted type of battery in the electric vehicle industry because of some of their positive inherent characteristics. However, the safety problems associated with inaccurate estimation and prediction of the state of health of these batteries have attracted wide attention due to the adverse negative effect on vehicle safety. In this paper, both machine and deep learning models were used to estimate the state of health of lithium-ion batteries. The paper introduces the definition of battery health status and its importance in the electric vehicle industry. Based on the data preprocessing and visualization analysis, three features related to actual battery capacity degradation are extracted from the data. Two learning models, SVR and LSTM were employed for the state of health estimation and their respective results are compared in this paper. The mean square error and coefficient of determination were the two metrics for the performance evaluation of the models. The experimental results indicate that both models have high estimation results. However, the metrics indicated that the SVR was the overall best model. 展开更多
关键词 Support Vector Regression (SVR) Long Short-Term Memory (LSTM) Network State of Health (SOH) Estimation
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基于NBM-LSTM融合模型的超短期光伏发电预测 认领 引用
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作者 汤海波 徐晓亮 《电力大数据》 2026年第6期1-9,共9页
精确的光伏功率预测对保障电力系统稳定运行至关重要。光伏功率预测精确度很大程度上受天气类型影响,动态变化的天气特征会显著降低模型的预测精确度。针对这一问题,提出了一种融合朴素贝叶斯(naive Bayes model,NBM)和长短记忆(long sh... 精确的光伏功率预测对保障电力系统稳定运行至关重要。光伏功率预测精确度很大程度上受天气类型影响,动态变化的天气特征会显著降低模型的预测精确度。针对这一问题,提出了一种融合朴素贝叶斯(naive Bayes model,NBM)和长短记忆(long short-term memory neural network,LSTM)神经网络的光伏功率超短期预测模型。所提方法首先基于气象因素,采用NBM对动态变化的天气类型进行预判;再通过Stacking的集成方式将天气类型信息引入LSTM模型,实现计及天气类型的光伏发电功率96点超短期预测。实验结果表明,提出的融合模型可有效提升各类型天气情况下的光伏功率预测精度,对提高可再生能源渗透率具有积极作用。 展开更多
关键词 光伏功率预测 朴素贝叶斯模型 长短记忆神经网络 天气类型 Stacking
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Real-time UAV path planning based on LSTM network 认领 引用 被引量：9
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作者 ZHANG Jiandong GUO Yukun +3 位作者 ZHENG Lihui YANG Qiming SHI Guoqing WU Yong 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2024年第2期374-385,共12页
To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on... To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning. 展开更多
关键词 deep Q network path planning neural network unmanned aerial vehicle(UAV) long short-term memory(LSTM)
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Behavior recognition based on the fusion of 3D-BN-VGG and LSTM network 认领 引用 被引量：5
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作者 Wu Jin Min Yu +2 位作者 Shi Qianwen Zhang Weihua Zhao Bo 《High Technology Letters》 EI CAS 2020年第4期372-382,共11页
In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dime... In order to effectively solve the problems of low accuracy,large amount of computation and complex logic of deep learning algorithms in behavior recognition,a kind of behavior recognition based on the fusion of 3 dimensional batch normalization visual geometry group(3D-BN-VGG)and long short-term memory(LSTM)network is designed.In this network,3D convolutional layer is used to extract the spatial domain features and time domain features of video sequence at the same time,multiple small convolution kernels are stacked to replace large convolution kernels,thus the depth of neural network is deepened and the number of network parameters is reduced.In addition,the latest batch normalization algorithm is added to the 3-dimensional convolutional network to improve the training speed.Then the output of the full connection layer is sent to LSTM network as the feature vectors to extract the sequence information.This method,which directly uses the output of the whole base level without passing through the full connection layer,reduces the parameters of the whole fusion network to 15324485,nearly twice as much as those of 3D-BN-VGG.Finally,it reveals that the proposed network achieves 96.5%and 74.9%accuracy in the UCF-101 and HMDB-51 respectively,and the algorithm has a calculation speed of 1066 fps and an acceleration ratio of 1,which has a significant predominance in velocity. 展开更多
关键词 behavior recognition deep learning 3 dimensional batch normalization visual geometry group(3D-BN-VGG) long short-term memory(LSTM)network
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融合类Stacking算法的杭州臭氧浓度预测 认领 引用 被引量：12
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作者 董红召 郭红梅 应方 《环境科学》 EI CAS CSCD 北大核心 2024年第9期5188-5195,共8页
针对目前单机器学习模型对臭氧日均浓度预测精度较低的问题,提出一种融合类Stacking算法的臭氧浓度预测方法(FSOP),将统计方法普通最小二乘法(OLS)与机器学习算法相融合,通过集成不同学习器的优势来提高臭氧浓度预测模型的预测精度.采... 针对目前单机器学习模型对臭氧日均浓度预测精度较低的问题,提出一种融合类Stacking算法的臭氧浓度预测方法(FSOP),将统计方法普通最小二乘法(OLS)与机器学习算法相融合,通过集成不同学习器的优势来提高臭氧浓度预测模型的预测精度.采用杭州市2017年1月至2022年12月臭氧日最大8h浓度平均值的观测数据和气象再分析数据,根据Stacking算法的原理,先分别建立基于轻量级梯度提升机(LightGBM)算法、长短期记忆模型(LSTM)和Informer模型的特定臭氧浓度预测模型,再将以上模型的预测结果作为元特征,利用OLS算法获取臭氧浓度的预测表达式对臭氧浓度观测值进行拟合.结果表明,融合类Stacking算法后的模型预测精度获得提升,臭氧浓度拟合效果更好.其中,R2、RMSE和MAE分别为0.84、19.65μg·m−3和15.50μg·m−3,较单个机器学习模型预测精度提升了8%左右. 展开更多
关键词 类Stacking算法 轻量级梯度提升机(LightGBM)算法 长短期记忆模型(LSTM) Informer模型 普通最小二乘法(OLS)
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基于多模型融合Stacking集成学习的油田产量预测 认领 引用 被引量：6
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作者 张庭婷 潘美琪 +5 位作者 朱天怡 曹煜 张站权 刘单珂 贺兴 于立军 《科技和产业》 2023年第2期263-271,共9页
基于机器学习前沿理论,提出一种基于多模型融合Stacking集成学习方式的组合预测方法,以国内某特高含水油田区块中多口水驱产油井历年生产历史数据为试验样本,预测其动态产油量。依据不同算法的训练原理,选取极限梯度提升树算法、长短记... 基于机器学习前沿理论,提出一种基于多模型融合Stacking集成学习方式的组合预测方法,以国内某特高含水油田区块中多口水驱产油井历年生产历史数据为试验样本,预测其动态产油量。依据不同算法的训练原理,选取极限梯度提升树算法、长短记忆网络(LSTM)、时域卷积网络(TCN)等作为模型的基学习器,采用多元线性回归作为模型的元学习器。结果表明:融合后的Stacking模型充分发挥了各基学习器的优势,相比单一模型,融合后的Stacking模型预测平均误差较小,预测鲁棒性较好。该模型的提出对融合模型在特高含水油藏开发方面具有重要的应用意义。 展开更多
关键词 多模型融合 Stacking集成学习 极限梯度提升树 长短期记忆网络 时域卷积网络 产量预测
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Behavior recognition algorithm based on the improved R3D and LSTM network fusion 认领 引用 被引量：1
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作者 Wu Jin An Yiyuan +1 位作者 Dai Wei Zhao Bo 《High Technology Letters》 EI CAS 2021年第4期381-387,共7页
Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the... Because behavior recognition is based on video frame sequences,this paper proposes a behavior recognition algorithm that combines 3D residual convolutional neural network(R3D)and long short-term memory(LSTM).First,the residual module is extended to three dimensions,which can extract features in the time and space domain at the same time.Second,by changing the size of the pooling layer window the integrity of the time domain features is preserved,at the same time,in order to overcome the difficulty of network training and over-fitting problems,the batch normalization(BN)layer and the dropout layer are added.After that,because the global average pooling layer(GAP)is affected by the size of the feature map,the network cannot be further deepened,so the convolution layer and maxpool layer are added to the R3D network.Finally,because LSTM has the ability to memorize information and can extract more abstract timing features,the LSTM network is introduced into the R3D network.Experimental results show that the R3D+LSTM network achieves 91%recognition rate on the UCF-101 dataset. 展开更多
关键词 behavior recognition three-dimensional residual convolutional neural network(R3D) long short-term memory(LSTM) dropout batch normalization(BN)
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Dynamic Hand Gesture Recognition Based on Short-Term Sampling Neural Networks 认领 引用 被引量：16
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作者 Wenjin Zhang Jiacun Wang Fangping Lan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第1期110-120,共11页
Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning netwo... Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning network for hand gesture recognition.The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation.To learn short-term features,each video input is segmented into a fixed number of frame groups.A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot.These two entities are fused and fed into a convolutional neural network(Conv Net)for feature extraction.The Conv Nets for all groups share parameters.To learn longterm features,outputs from all Conv Nets are fed into a long short-term memory(LSTM)network,by which a final classification result is predicted.The new model has been tested with two popular hand gesture datasets,namely the Jester dataset and Nvidia dataset.Comparing with other models,our model produced very competitive results.The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures. 展开更多
关键词 Convolutional neural network(ConvNet) hand gesture recognition long short-term memory(LSTM)network short-term sampling transfer learning
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堆叠式LSTM组合模型的充电站用电量预测方法 认领 引用 被引量：1
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作者 王彩玲 丁当 《计算机时代》 2025年第1期1-4,共4页
随着电动汽车的普及,充电站对电力需求预测的精确性日益提高。本文设计了堆叠式LSTM模型,使用预处理过的某电动汽车充电站用电量数据,对比分析传统模型和LSTM模型在不同评估指标上的表现,验证所提出模型的优越性;还对多层堆叠式LSTM模... 随着电动汽车的普及,充电站对电力需求预测的精确性日益提高。本文设计了堆叠式LSTM模型,使用预处理过的某电动汽车充电站用电量数据,对比分析传统模型和LSTM模型在不同评估指标上的表现,验证所提出模型的优越性;还对多层堆叠式LSTM模型进行训练和测试,分析不同层数LSTM模型的性能,实验结果表明,三层堆叠式LSTM模型优于其他模型,能够显著提高用电量预测的准确度。 展开更多
关键词 用电量预测 长短期记忆网络 卷积神经网络-长短期记忆网络 堆叠式LSTM模型
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Anti⁃acceleration Saturation Terminal Guidance Law Based on LSTM 认领 引用
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作者 LI Guilin ZHOU Wei ZHANG Jiarui 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2025年第4期541-553,共13页
Missile acceleration saturation in a practical terminal guidance process may significantly reduce the interception performance.To solve this problem,this paper presents an anti-saturation guidance law with finite-time... Missile acceleration saturation in a practical terminal guidance process may significantly reduce the interception performance.To solve this problem,this paper presents an anti-saturation guidance law with finite-time convergence for a three dimensional maneuvering interception.The finite time boundedness(FTB)theory and the input-output finite time stability(IO-FTS)theory are used,as well as the long short-term memory(LSTM)network.A sufficient condition for FTB and IO-FTS of a class of nonlinear systems is given.Then,an anti-acceleration saturation missile terminal guidance law based on LSTM,namely LSTM-ASGL,is designed.It can effectively suppress the effect of acceleration saturation to track the maneuvering target more accurately in the complex dynamic environment.The excellent performance of LSTM-ASGL in different maneuvering target scenarios is verified by simulation.The simulation results show that the guidance law successfully prevents acceleration saturation and improves the tracking ability of the missile system to the maneuvering target.It is also shown that LSTM-ASGL has good generalization and anti-jamming performance,and consumes less energy than the anti-acceleration saturation terminal guidance law. 展开更多
关键词 long short-term memory network(LSTM) anti-acceleration saturation terminal guidance law finite-time boundedness(FTB) input-output finite time stability(IO-FTS)
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