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Research on remaining useful life prediction of rolling bearings based on adaptive variational mode decomposition and dual-branch temporal neural network 认领 引用
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作者 Wuchu Tang Wenxin Dong +1 位作者 Jiawei Yang Guofu Wu 《Advances in Engineering Innovation》 2026年第7期12-30,共19页
To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction fr... To address the nonlinear and non-stationary characteristics of vibration signals during rolling bearing operation and the issue of insufficient degradation information representation,this paper designs a prediction framework that combines Adaptive Variational Mode Decomposition(AVMD)with a SETCNBiGRU multi-head temporal attention mechanism for Remaining Useful Life(RUL)prediction.First,AVMD is used to decompose the bearing horizontal vibration signal into five Intrinsic Mode Functions(IMFs).Timedomain and frequency-domain statistics are extracted from each IMF and concatenated into a 115-dimensional degradation feature sequence.Subsequently,the model processes in parallel:a TCN-SENet branch extracts local temporal features and adaptively adjusts channel weights,while a BiGRU with multi-head temporal attention sub-network captures global bidirectional dependencies and critical degradation periods within the degradation sequence.Finally,the two types of features are fused,and the RUL prediction result is output.Experimental results demonstrate that the proposed model achieves an RMSE,MAE,and R2of 0.0582,0.0477,and 0.9483 respectively on the IEEE PHM 2012 dataset,and average values of 0.0780,0.0559,and 0.9133 on a self-built laboratory bearing dataset,indicating good prediction accuracy,robustness,and generalization ability. 展开更多
关键词 remaining useful life prediction adaptive variational mode decomposition temporal convolutional network bidirectional gated recurrent unit multi-head temporal attention mechanism
Forecasting of ROTI index in polar regions using BWO-VMD-LSTM hybrid model 认领 引用
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作者 YeWen Wu Kang Gong +8 位作者 JianJun Liu ZhiWei Wang LiGuo Zhang Ming Ou Jing Chen Lin Ni HuiXuan Qiu XiangCai Chen Run Shi 《Earth and Planetary Physics》 EI CSCD 2026年第4期571-582,共12页
The Rate of TEC Index(ROTI),a crucial indicator of fluctuations in the ionospheric Total Electron Content(TEC),has long served as an effective proxy for characterizing ionospheric activity.However,ROTI sequences exhib... The Rate of TEC Index(ROTI),a crucial indicator of fluctuations in the ionospheric Total Electron Content(TEC),has long served as an effective proxy for characterizing ionospheric activity.However,ROTI sequences exhibit significant nonstationary and nonlinear characteristics,which intensify during substorms,making high-accuracy prediction challenging for traditional models.To address these complications,we propose a hybrid forecasting model,named BWO-VMD-LSTM,that integrates the Beluga Whale Optimization(BWO)algorithm,Variational Mode Decomposition(VMD),and Long Short-Term Memory(LSTM)networks.Our methodology first employs BWO to adaptively optimize the key parameters of VMD,achieving an optimal decomposition configuration.The optimized VMD then decomposes the original non-stationary ROTI sequence into several relatively stable Intrinsic Mode Functions(IMFs).Subsequently,an LSTM model is constructed to independently forecast each IMF.Finally,the predictions of all components are reconstructed to produce the final ROTI forecast.Experimental results demonstrate that our model outperforms baseline models under various geomagnetic conditions(including quiet,normal,and substorm periods),showing significant improvements in key metrics such as Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE),thereby exhibiting superior prediction accuracy and stability.This study presents an effective and robust tool for high-precision ROTI forecasting,with promising potential for enhancing space weather monitoring and scintillation warning services. 展开更多
关键词 ROTI forecasting polar ionosphere Long Short-Term Memory Beluga Whale Optimization Variational Mode Decomposition
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Hybrid deep learning model with VMD-BiLSTM-GRU networks for short-term traffic flow prediction 认领 引用 被引量：1
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作者 Changxi Ma Yanming Hu Xuecai Xu 《Data Science and Management》 EI CSCD 2025年第3期257-269,共13页
Accelerating urbanization and the rapid development of intelligent transportation systems have rendered shortterm traffic flow prediction an important research field.Accurate prediction of traffic flow is beneficial f... Accelerating urbanization and the rapid development of intelligent transportation systems have rendered shortterm traffic flow prediction an important research field.Accurate prediction of traffic flow is beneficial for the optimization of traffic planning,improvement of road utilization,reduction of traffic congestion,and reduction in the incidence of traffic accidents.However,data pertaining to traffic flow are typically influenced by a multitude of factors,resulting in data that exhibit a considerable degree of nonlinearity and complexity.To address the issue of noise in raw traffic flow data,this study proposes a hybrid model that combines variational mode decomposition(VMD),a bidirectional long short-term memory network(BiLSTM),and a gated recurrent unit(GRU)for short-term traffic flow prediction.To validate the effectiveness of the model,an experimental validation was conducted based on traffic flow data from UK highways,and the performance of the model was compared with common benchmark models.The experimental results demonstrate that the proposed method yields superior prediction results in terms of mean absolute error,coefficient of determination,and root-mean-square error compared to existing prediction techniques,thereby substantiating its efficacy in short-term traffic flow prediction. 展开更多
关键词 Deep learning Traffic flow prediction Variational mode decomposition Bi-directional long short-term memory networks Gated recurrent units
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Microseismic signal denoising by combining variational mode decomposition with permutation entropy 认领 引用 被引量：10
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作者 Zhang Xing-Li Cao Lian-Yue +2 位作者 Chen Yan Jia Rui-Sheng Lu Xin-Ming 《Applied Geophysics》 SCIE CSCD 2022年第1期65-80,144,145,共16页
Remarkable progress has been achieved on microseismic signal denoising in recent years,which is the basic component for rock-burst detection.However,its denoising effectiveness remains unsatisfactory.To extract the ef... Remarkable progress has been achieved on microseismic signal denoising in recent years,which is the basic component for rock-burst detection.However,its denoising effectiveness remains unsatisfactory.To extract the effective microseismic signal from polluted noisy signals,a novel microseismic signal denoising method that combines the variational mode decomposition(VMD)and permutation entropy(PE),which we denote as VMD–PE,is proposed in this paper.VMD is a recently introduced technique for adaptive signal decomposition,where K is an important decomposing parameter that determines the number of modes.VMD provides a predictable eff ect on the nature of detected modes.In this work,we present a method that addresses the problem of selecting an appropriate K value by constructing a simulation signal whose spectrum is similar to that of a mine microseismic signal and apply this value to the VMD–PE method.In addition,PE is developed to identify the relevant effective microseismic signal modes,which are reconstructed to realize signal filtering.The experimental results show that the VMD–PE method remarkably outperforms the empirical mode decomposition(EMD)–VMD filtering and detrended fl uctuation analysis(DFA)–VMD denoising methods of the simulated and real microseismic signals.We expect that this novel method can inspire and help evaluate new ideas in this field. 展开更多
关键词 Denoising Microseismic signal Permutation entropy Variational mode decomposition
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Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals 认领 引用 被引量：6
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作者 Muhd Firdaus Isham Muhd Salman Leong +1 位作者 Meng Hee Lim Zair Asrar Ahmad 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2018年第1期38-50,共13页
The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for e... The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications. 展开更多
关键词 variational mode decomposition(VMD) monitoring diagnosis vibration signal mode number gear
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基于改进VMD和SVM方法的滚动轴承故障诊断 认领 引用 被引量：3
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作者 何晓良 苏春 张玉茹 《东南大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第2期322-332,共11页
为解决旋转部件早期振动故障信号存在的特征微弱、非平稳等问题,提出一种基于改进变分模态分解(VMD)及支持向量机(SVM)的故障诊断方法。采用改进的野马算法(IWHO)优化VMD中的惩罚因子α和模态数K以实现参数自动寻优,采用适应度函数选择... 为解决旋转部件早期振动故障信号存在的特征微弱、非平稳等问题,提出一种基于改进变分模态分解(VMD)及支持向量机(SVM)的故障诊断方法。采用改进的野马算法(IWHO)优化VMD中的惩罚因子α和模态数K以实现参数自动寻优,采用适应度函数选择最小包络熵。利用优化后的VMD完成振动信号分解,得到振动信号的固有模态函数(IMF)。在此基础上,采用峭度准则选取前5阶IMF分量以计算时频域特征,构建特征向量;将特征向量输入SVM中完成训练,实现旋转部件的故障分类。以滚动轴承试验数据集为例,验证方法有效性。结果表明:所提出的方法能有效处理非平稳振动信号,针对数据集中轴承4种运行状态诊断的准确率达99.17%;在模拟噪声干扰环境下,模型仍能保持95.8%以上的诊断精度。 展开更多
关键词 变分模态分解 支持向量机 改进野马算法 故障诊断
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基于优化VMD二次分解的短期电力负荷预测 认领 引用 被引量：2
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作者 蒋建东 韩文轩 +3 位作者 赵云飞 燕跃豪 鲍薇 刘晓辉 《郑州大学学报（工学版）》 CAS 北大核心 2026年第1期124-130,共7页
针对台区配变负荷数据复杂度高、波动性强的特点,提出了一种基于二次分解和时间卷积网络的短期电力负荷预测模型。首先,使用最大互信息系数法对高维特征的负荷数据集进行特征提取;其次,采用完全自适应噪声集合经验模态分解和优化变分模... 针对台区配变负荷数据复杂度高、波动性强的特点,提出了一种基于二次分解和时间卷积网络的短期电力负荷预测模型。首先,使用最大互信息系数法对高维特征的负荷数据集进行特征提取;其次,采用完全自适应噪声集合经验模态分解和优化变分模态分解对配变负荷数据进行二次分解;再次,将两次分解得到的子序列输入时间卷积网络模型中进行预测;最后,将各子序列的预测结果叠加,得到最终的负荷预测结果。在郑州市某台区配变负荷数据上进行仿真分析,与传统时间卷积网络模型相比,所提模型MAE、MAPE和RMSE分别减少了64.29%,9.66百分点和59.00%。实验结果表明,所提组合预测模型具有更好的预测效果和更高的预测精度。 展开更多
关键词 二次分解 负荷预测 完全自适应噪声集合经验模态分解 变分模态分解 时间卷积网络
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一种基于信号分解质量多目标评价的VMD参数寻优新方法 认领 引用 被引量：1
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作者 李贺 查志华 吴杰 《振动与冲击》 EI CSCD 北大核心 2026年第3期88-96,共9页
模态数K和惩罚因子α设置不当,会严重影响信号变分模态分解(variational mode decomposition,VMD)的性能,已有的改进VMD法参数寻优时,无法同时兼顾速度和准确性,也未将避免信号欠分解和过分解以及分量与原信号的信息量差异最小都作为寻... 模态数K和惩罚因子α设置不当,会严重影响信号变分模态分解(variational mode decomposition,VMD)的性能,已有的改进VMD法参数寻优时,无法同时兼顾速度和准确性,也未将避免信号欠分解和过分解以及分量与原信号的信息量差异最小都作为寻优的目标,导致所确定的K和α最优组合未能充分提升信号分解性能。针对此问题,提出了一种信号变步长VMD (variable step size-VMD,VSS-VMD)算法对K和α寻优,采用能量损失系数评价信号欠分解,互相关系数和峭度相结合评价信号过分解,分量与原信号的信息熵差评价分量表征原信号能力,α以较大初始步长逐渐变小,先以较大步长快速找到较优参数组合并缩小寻优范围,然后以较小步长精确找到最优参数组合。与最近报道的3种改进VMD法相比,VSS-VMD法确定的最优K和α对仿真信号和多个实测信号分解结果表明,提取信号分量完备,未发生欠分解,有效避免了过分解,同时表现出更优的噪声抑制效果,分量与原信号的能量差异值以及分量之间的正交指数都低,分量表征原信号的能力强。该方法在参数寻优范围很大的情况下,确保准确寻优的同时,寻优时间明显减少,为信号VMD性能提升以及有关应用研究提供了重要参考和借鉴。 展开更多
关键词 变分模态分解(VMD) 惩罚因子 变步长 过分解 欠分解 信号分量信息
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基于BWO优化VMD和KELM的柔性直流输电线路短路故障定位方法 认领 引用 被引量：3
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作者 赵岩 王梓毅 徐天 《南方电网技术》 CSCD 北大核心 2026年第3期8-18,31,共11页
针对行波波头标定的精度不足以及智能定位模型拟合性能易受参数影响的问题,提出了一种基于白鲸算法优化变分模态分解和核极限学习机的柔性直流输电线路短路故障定位方法。首先,采用白鲸算法优化变分模态分解的参数,结合小波软阈值去噪... 针对行波波头标定的精度不足以及智能定位模型拟合性能易受参数影响的问题,提出了一种基于白鲸算法优化变分模态分解和核极限学习机的柔性直流输电线路短路故障定位方法。首先,采用白鲸算法优化变分模态分解的参数,结合小波软阈值去噪方法对采集的故障信号进行降噪和分解,再结合希尔伯特变换标定初始行波的到达时刻。其次,将行波的到达时刻作为特征值构建特征数据集,用白鲸算法优化核极限学习机定位模型。最后,将数据集代入到优化后的定位模型中实现故障定位。结果表明,该方法的定位模型拟合程度达到99.4%,具有较高的定位精度和较好的鲁棒性,所提方法对噪声和过渡电阻的耐受性能较高,定位误差在500 m以内。 展开更多
关键词 柔性直流输电线路 变分模态分解 白鲸算法 核极限学习机 故障定位
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应用VMD-Transformer-ResLSTM的短期天然气负荷预测 认领 引用 被引量：2
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作者 赵明智 郭光荣 +2 位作者 范立军 韩龙 于千城 《中国测试》 CAS 北大核心 2026年第3期144-153,共10页
由于城市居民的消费习惯和生活节奏的变化,精确预测天然气消耗量变得尤为重要。为此,文章设计一种基于VMD-Transformer-ResLSTM的混合神经网络模型。首先,通过变分模态分解(VMD)将原始负荷序列分解为本征模态函数(IMF)分量,从而避免模... 由于城市居民的消费习惯和生活节奏的变化,精确预测天然气消耗量变得尤为重要。为此,文章设计一种基于VMD-Transformer-ResLSTM的混合神经网络模型。首先,通过变分模态分解(VMD)将原始负荷序列分解为本征模态函数(IMF)分量,从而避免模态混叠和假峰值的问题。随后,对Transformer解码层进行重新构建,并将其与LSTM网络融合,旨在更好地捕捉序列中的长期依赖关系,同时减少模型参数。为解决LSTM网络中常见的梯度消失和梯度爆炸问题,文章引入残差连接机制,将其整合到Transformer和LSTM网络中。其次,为进一步提升预测精度,设计一个误差修正模块,以提高天然气负荷预测的稳定性和准确性。实验结果表明,该组合模型在预测精度上显著优于传统模型如ARIMA、Transformer、GRU和LSTM,预测的平均绝对误差(MAE)提升23%~58%。综上所述,该方法可显著提升天然气负荷预测的精度。 展开更多
关键词 天然气短期负荷预测 变分模态分解 Transformer LSTM 残差连接 误差修正
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基于IGJO-VMD-SVM的电机轴承故障诊断 认领 引用 被引量：4
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作者 回立川 曹威 +1 位作者 闫康 王久阳 《控制工程》 CSCD 北大核心 2026年第5期925-934,共10页
为解决有噪环境下轴承故障特征提取难、诊断准确率低的问题,提出了一种结合改进金豺优化(golden jackal optimization,GJO)算法、变分模态分解(variational mode decomposition,VMD)和支持向量机(support vector machine,SVM)的轴承故... 为解决有噪环境下轴承故障特征提取难、诊断准确率低的问题,提出了一种结合改进金豺优化(golden jackal optimization,GJO)算法、变分模态分解(variational mode decomposition,VMD)和支持向量机(support vector machine,SVM)的轴承故障诊断方法。首先,针对GJO算法易陷入局部最优的问题,引入Tent混沌映射、非线性递减参数、反向学习策略和自适应权重对其进行改进;其次,针对故障信号易被噪声淹没和特征提取难的问题,利用VMD将经过奇异谱分析(singular spectrum analysis,SSA)降噪后的信号分解,对噪声进行抑制,接着采用t分布-随机邻域嵌入(t-distribution-random neighborhood embedding,t-SNE)对提取的特征进行筛选,挑选有用特征;最后,针对SVM参数设置问题,使用改进GJO算法解决并构造诊断模型。结果表明,在相同的实验条件下,该模型的诊断准确率高于其他算法优化的诊断模型。 展开更多
关键词 轴承故障诊断 奇异谱分析 变分模态分解 金豺优化算法 t分布-随机邻域嵌入 支持向量机
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基于RIME-VMD联合小波阈值的爆破振动信号去噪方法 认领 引用 被引量：2
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作者 王薇 程忠耀 +1 位作者 向延念 宋良俊 《铁道科学与工程学报》 EI CAS CSCD 北大核心 2026年第1期465-479,共15页
随着现代化建设的加速推进,邻近既有建筑的爆破作业日益增多,监测和分析爆破引起的振动对结构安全的评估至关重要。然而,爆破振动信号的非线性特性和复杂的环境因素干扰使得从实测信号中提取有效信号成分难度较大,给后续的信号分析造成... 随着现代化建设的加速推进,邻近既有建筑的爆破作业日益增多,监测和分析爆破引起的振动对结构安全的评估至关重要。然而,爆破振动信号的非线性特性和复杂的环境因素干扰使得从实测信号中提取有效信号成分难度较大,给后续的信号分析造成了较大影响。为提高爆破振动信号的降噪精度,将雾凇优化算法(RIME)、变分模态分解(VMD)和小波阈值进行融合,形成一种爆破振动信号联合去噪方法。该方法首先通过雾凇优化算法对VMD关键参数进行优化,然后通过优化后的VMD对振动信号进行自适应分解,剔除方差贡献率较低的分量,再采用小波阈值对筛选后的分量进行降噪处理,最终重构得到去噪后的信号。对该方法的降噪效果进行仿真分析和实际工程验证,结果表明:在仿真信号分析中,经RIME-VMD联合小波阈值的降噪方法去噪后的信号与无噪声的纯净信号相比,形状与特征高度吻合,且信噪比(SNR)和均方根误差(RMSE)等去噪指标优于EMD、小波阈值、EMD联合小波阈值等常用去噪方法;经工程实际案例验证,该方法能够在极大保留原信号基本特征的前提下,有效去除爆破振动信号中的高频噪声,降噪后信号更加符合爆破振动信号的主频范围,且具有比EMD、小波阈值、EMD联合小波阈值等常用去噪方法更好的去噪效果。该研究成果对爆破振动信号的降噪处理具有参考意义。 展开更多
关键词 爆破振动 信号处理 联合降噪 雾凇优化算法 变分模态分解 小波阈值去噪
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Removal of Ocular Artifacts from Electroencephalo-Graph by Improving Variational Mode Decomposition 认领 引用 被引量：1
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作者 Miao Shi Chao Wang +3 位作者 Wei Zhao Xinshi Zhang Ye Ye Nenggang Xie 《China Communications》 SCIE CSCD 2022年第2期47-61,共15页
Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing metho... Ocular artifacts in Electroencephalography(EEG)recordings lead to inaccurate results in signal analysis and process.Variational Mode Decomposition(VMD)is an adaptive and completely nonrecursive signal processing method.There are two parameters in VMD that have a great influence on the result of signal decomposition.Thus,this paper studies a signal decomposition by improving VMD based on squirrel search algorithm(SSA).It’s improved with abilities of global optimal guidance and opposition based learning.The original seasonal monitoring condition in SSA is modified.The feedback of whether the optimal solution is successfully updated is used to establish new seasonal monitoring conditions.Opposition-based learning is introduced to reposition the position of the population in this stage.It is applied to optimize the important parameters of VMD.GOSSA-VMD model is established to remove ocular artifacts from EEG recording.We have verified the effectiveness of our proposal in a public dataset compared with other methods.The proposed method improves the SNR of the dataset from-2.03 to 2.30. 展开更多
关键词 ocular artifact variational mode decomposition squirrel search algorithm global guidance ability opposition-based learning
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Spatio-Temporal Wind Speed Prediction Based on Variational Mode Decomposition 认领 引用 被引量：1
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作者 Yingnan Zhao Guanlan Ji +2 位作者 Fei Chen Peiyuan Ji Yi Cao 《Computer Systems Science & Engineering》 SCIE EI 2022年第11期719-735,共17页
Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal netw... Improving short-term wind speed prediction accuracy and stability remains a challenge for wind forecasting researchers.This paper proposes a new variational mode decomposition(VMD)-attention-based spatio-temporal network(VASTN)method that takes advantage of both temporal and spatial correlations of wind speed.First,VASTN is a hybrid wind speed prediction model that combines VMD,squeeze-and-excitation network(SENet),and attention mechanism(AM)-based bidirectional long short-term memory(BiLSTM).VASTN initially employs VMD to decompose the wind speed matrix into a series of intrinsic mode functions(IMF).Then,to extract the spatial features at the bottom of the model,each IMF employs an improved convolutional neural network algorithm based on channel AM,also known as SENet.Second,it combines BiLSTM and AM at the top layer to extract aggregated spatial features and capture temporal dependencies.Finally,VASTN accumulates the predictions of each IMF to obtain the predicted wind speed.This method employs VMD to reduce the randomness and instability of the original data before employing AM to improve prediction accuracy through mapping weight and parameter learning.Experimental results on real-world data demonstrate VASTN’s superiority over previous related algorithms. 展开更多
关键词 Short-term wind speed prediction variational mode decomposition attention mechanism SENet BiLSTM
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Underwater acoustic signal denoising model based on secondary variational mode decomposition 认领 引用 被引量：1
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作者 Hong Yang Wen-shuai Shi Guo-hui Li 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2023年第10期87-110,共24页
Due to the complexity of marine environment,underwater acoustic signal will be affected by complex background noise during transmission.Underwater acoustic signal denoising is always a difficult problem in underwater ... Due to the complexity of marine environment,underwater acoustic signal will be affected by complex background noise during transmission.Underwater acoustic signal denoising is always a difficult problem in underwater acoustic signal processing.To obtain a better denoising effect,a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm(BVMD),fluctuation-based dispersion entropy threshold improved by Otsu method(OFDE),cosine similarity stationary threshold(CSST),BVMD,fluctuation-based dispersion entropy(FDE),named BVMD-OFDE-CSST-BVMD-FDE,is proposed.In the first place,decompose the original signal into a series of intrinsic mode functions(IMFs)by BVMD.Afterwards,distinguish pure IMFs,mixed IMFs and noise IMFs by OFDE and CSST,and reconstruct pure IMFs and mixed IMFs to obtain primary denoised signal.In the end,decompose primary denoising signal into IMFs by BVMD again,use the FDE value to distinguish noise IMFs and pure IMFs,and reconstruct pure IMFs to obtain the final denoised signal.The proposed mothod has three advantages:(i)BVMD can adaptively select the decomposition layer and penalty factor of VMD.(ii)FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs,and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds.(iii)Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of noise.The chaotic signal and real ship signal are denoised.The experiment result shows that the proposed method can effectively denoise.It improves the denoising effect after primary decomposition,and has good practical value. 展开更多
关键词 Underwater acoustic signal Denoising Variational mode decomposition Secondary decomposition Fluctuation-based dispersion entropy Cosine similarity
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基于BWO-VMD-ISSA-LSTM的交通运输业碳排放预测研究 认领 引用 被引量：1
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作者 王庆荣 王俊杰 +3 位作者 朱昌锋 张金鹏 何润田 刘心康 《计算机工程与应用》 EI CSCD 北大核心 2026年第10期376-388,共13页
针对交通运输业碳排放量的非线性影响预测精度的问题,提出了一种结合白鲸优化算法(beluga whale optimization,BWO)、变分模态分解(variational mode decomposition,VMD)、改进麻雀搜索算法(improved sparrow search algorithm,ISSA)及L... 针对交通运输业碳排放量的非线性影响预测精度的问题,提出了一种结合白鲸优化算法(beluga whale optimization,BWO)、变分模态分解(variational mode decomposition,VMD)、改进麻雀搜索算法(improved sparrow search algorithm,ISSA)及LSTM的碳排放预测模型。引入最大互信息系数(maximum information coefficient,MIC)提取影响碳排放量的主要因素,剔除冗余特征。利用BWO对VMD的分解模态数和惩罚因子寻优,增强两参数间的协调性,进而将碳排放量分解为不同频率的模态分量和剩余分量,削弱原始碳排放量的非线性;通过在LSTM的输入端嵌入特征注意力机制(feature attention mechanism,FA),突出关键输入特征。引入基于改进Tent混沌映射、动态步长权重因子、混合变异算子和精英反向学习的混合策略改进SSA算法,避免算法陷入局部最优。对各模态分量分别构建基于ISSA-LSTM的预测模型,并对预测结果进行集成。采用中国交通运输业1990—2019年的碳排放数据对模型进行验证,结果表明,所提模型较最优对比模型的RMSE、MAE和MAPE分别降低了26.28%、31.64%和33.32%,能够有效地预测交通运输业碳排放量。 展开更多
关键词 交通运输业 碳排放预测 白鲸优化算法 变分模态分解 麻雀搜索算法 最大互信息系数
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基于VMD-WHHO-BLS的无人船位姿预测 认领 引用 被引量：1
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作者 葛泉波 薛子建 +1 位作者 张明川 吴庆涛 《控制理论与应用》 EI CAS CSCD 北大核心 2026年第2期335-347,共13页
随着人工智能技术的发展,在无人控制系统领域中,智能传感器的普及使得各式的无人装备运行数据更加的丰富.水面无人船作为无人智能装备的重要组成部分,其关键环节就在于对其安全稳定的自主控制,因为其结构复杂,并且要长时间在未知的环境... 随着人工智能技术的发展,在无人控制系统领域中,智能传感器的普及使得各式的无人装备运行数据更加的丰富.水面无人船作为无人智能装备的重要组成部分,其关键环节就在于对其安全稳定的自主控制,因为其结构复杂,并且要长时间在未知的环境运作,难免出现各种异常状态,会直接影响无人装备的工作能力,降低其安全性和经济性,所以对无人船的位姿状态进行精确的预测十分必要.本文先利用变分模态分解将时间序列数据分解成若干分量,再采用基于宽度学习系统的方法对无人船中的几类数据进行了预测,同时用基于鲸鱼算法与模拟退火算法改进的哈里斯鹰优化算法对宽度学习中的伪逆求解回归参数进行优化.经仿真实验证明,该方法在预测的准确性和训练速度方面都有一定优势. 展开更多
关键词 无人船 宽度学习系统 位姿预测 变分模态分解 哈里斯鹰优化 鲸鱼群算法
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基于VMD-CNN-BiLSTM模型的短时交通流预测 认领 引用 被引量：1
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作者 李博 张传煌 +1 位作者 于晓 张旭东 《武汉理工大学学报（交通科学与工程版）》 2026年第2期250-257,共8页
文中研究使用滴滴盖亚数据提供的网约车轨迹数据集,经数据清洗等步骤后获取特定道路的交通流量.构建VMD-CNN-BiLSTM模型进行预测,模型结合变分模态分解(VMD)、卷积神经网络(CNN)和双向长短时记忆网络(BiLSTM)的优势,能有效捕捉交通流量... 文中研究使用滴滴盖亚数据提供的网约车轨迹数据集,经数据清洗等步骤后获取特定道路的交通流量.构建VMD-CNN-BiLSTM模型进行预测,模型结合变分模态分解(VMD)、卷积神经网络(CNN)和双向长短时记忆网络(BiLSTM)的优势,能有效捕捉交通流量的时序性和空间依赖性.结果表明:本模型在短时交通流量预测方面表现优异,与多种基准组合模型相比,在各项误差指标上均有显著降低,平均绝对误差(MAE)和均方根误差(RMSE)平均下降幅度分别达到17.26%和18.75%.此外,模型的判定系数R 2高达96.6503%,显示出较强的拟合能力和稳定性. 展开更多
关键词 城市交通 短时交通流预测 VMD-CNN-BiLSTM模型 变分模态分解 卷积神经网络
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绕射波GWO-VMD分离成像方法 认领 引用
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作者 林朋 刘育林 +3 位作者 彭苏萍 崔晓芹 郭偿波 杨婕 《煤田地质与勘探》 EI CAS CSCD 北大核心 2026年第2期183-191,共9页
【背景】断层、陷落柱和尖灭点等小尺度不连续地质体广泛存在于地下空间,与煤炭、油气等地下资源的安全生产及开发有密切联系。作为小尺度不连续地质体的波场响应,绕射波可以克服传统反射波成像的不足,具备对小尺度地质体高精度识别和... 【背景】断层、陷落柱和尖灭点等小尺度不连续地质体广泛存在于地下空间,与煤炭、油气等地下资源的安全生产及开发有密切联系。作为小尺度不连续地质体的波场响应,绕射波可以克服传统反射波成像的不足,具备对小尺度地质体高精度识别和定位的能力。【目的和方法】为实现不连续地质体绕射波成像,以反射波和绕射波在运动学和动力学特征差异为基础,利用变分模态分解(VMD)方法的精准时频域自适应分解能力和灰狼算法(GWO)的高效稳定全局寻优能力,有效避免了经验误差和局部最优问题,同时提高了绕射波分离的精度与方法的自适应性。【结果和结论】相较于鲸鱼算法(WOA)和蚁群算法(ACO),粒子群算法(PSO)、麻雀搜索算法(SSA)和灰狼算法(GWO)的最优适应度值较小(3.172),具有较好的寻优性能。此外,相较于粒子群算法(PSO)和麻雀搜索算法(SSA),灰狼算法(GWO)具有更小的迭代收敛次数,仅通过6次迭代即可收敛至全局最优。由此证明了GWO算法在寻优性能和寻优速度方面的优越性。通过合成数据和实际数据的测试,验证了GWO-VMD算法在绕射波分离和强反射压制方面的有效性,能够实现对微尺度构造的高分辨率成像。 展开更多
关键词 不连续地质体 绕射波分离 变分模态分解 参数寻优 灰狼算法
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基于参数优化的VMD和CWT结构密集模态参数识别 认领 引用
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作者 赵丽洁 孙子一 +2 位作者 王昊 解咏平 练继建 《振动与冲击》 EI CSCD 北大核心 2026年第4期51-60,共10页
针对变分模态分解的模态分解数K及二次惩罚因子α难以确定和连续小波变换对结构密集模态参数识别精度不高的问题,提出了一种基于参数优化变分模态分解(variational mode decomposition,VMD)与连续小波变换(continuous wavelet transform... 针对变分模态分解的模态分解数K及二次惩罚因子α难以确定和连续小波变换对结构密集模态参数识别精度不高的问题,提出了一种基于参数优化变分模态分解(variational mode decomposition,VMD)与连续小波变换(continuous wavelet transform,CWT)相结合的结构密集模态参数识别方法。以能量集中度与互信息构建全新综合目标函数,引入蜣螂优化算法自适应地搜寻最佳[K,α]参数组合;其次,基于最优[K,α]参数组合,对具有密集模态的振动响应信号进行VMD,结合皮尔逊相关系数指标筛选有效模态分量;最后,对有效模态分量进行CWT识别结构的模态频率和模态阻尼比。通过四自由度密集模态系统仿真算例表明,相比传统CWT算法,参数优化VMD结合CWT的方法,识别结构的密集模态参数精度更高,并具备一定的抗噪声性能;五层框架结构模型试验进一步验证了所提方法的实用性。 展开更多
关键词 模态参数识别 变分模态分解(VMD) 连续小波变换(CWT) 密集模态
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