搜索资源列表
IO_VMD
- 仿照EMD的分解层数确定方法，用以优化VMD的分解层数K(The method of determining the number of decomposition layers according to EMD is used to optimize the decomposition level of VMD K)
VMD优化
- 基于VMD的机车滚动轴承故障，变转速的测量，VMD的优化(Measurement of Rolling Bearing Fault and Variable Speed of Locomotive Based on VMD)
matlab代码
- 针对小波包去噪对含强白噪声的信号处理效果不理想问题，提出了基于互相关分析优化的VMD-小波包阈值去噪方法。该方法融合了VMD和小波包去噪的优势，通过VMD把含噪信号分解成若干个模态分量，根据互相关分析提出的临界相关系数从所有模态分量中搜寻极优模态分量，之后利用小波包阈值去噪对极优模态分量进行处理。实验结果表明，该方法对含强白噪声的信号去噪效果具有优势，能够保全信号的有效分量，克服了传统VMD去噪的盲目性，保证了去噪后信号的真实性。(Denoising by wavelet threshold t
pso-vmd
- 提供 基于粒子群算法优化的变分模态分解算法，适应度函数选择的是模糊熵(This paper presents a variational mode decomposition algorithm based on particle swarm optimization, and the selection of fitness function is fuzzy entropy)
优化K值
- 分解信号,能量差值优化K，峭度，样本熵，排列熵(Decomposition of signal, optimization of energy difference value K, kurtosis, sample entropy, permutation entropy)
奇异值确定K
- 根据奇异值分解出来的奇异值，画出奇异值分布曲线，根据公式算出奇异值的突变点，此时突变点即是VMD分解分量数的K值(According to the singular value decomposed by singular value, the distribution curve of singular value is drawn, and the mutation point of singular value is calculated according to the formula.
基于遗传算法优化VMD参数
- 基于遗传算法优化多尺度排列熵参数，类似于粒子群算法优化参数(Optimization of VMD parameters based on genetic algorithm)
灰狼GWOVMD
- 算法是基于灰狼优化算法GWO优化VMD，可以大大提高VMD的分类准确率，提高优化时间。(This algorithm is based on GWO optimization VMD, which can greatly improve VMD classification accuracy and optimization time.)
鸡群CSOSVM
- 本算法是基于鸡群优化算法CSO优化SVM，可以大大提高VMD的分类准确率，提高优化时间。(This algorithm is based on CSO to optimize SVM, which can greatly improve the classification accuracy of VMD and improve the optimization time.)
蝙蝠BASVM
- 本算法是基于蝙蝠优化算法BA优化SVM，可以大大提高VMD的分类准确率，提高优化时间。(This algorithm is based on bat optimization algorithm BA to optimize SVM, which can greatly improve the classification accuracy of VMD and improve the optimization time.)
Main_PSO_VMD
- 粒子群算法优化vmd算法，极大提高算法的精确性和运行速度(Particle swarm optimization algorithm optimizes VMD algorithm, which greatly improves the accuracy and running speed of the algorithm)