基于随机重心反向学习改进RBMO的VMD去噪方法OA

Variational modal decomposition(VMD)is an adaptive processing method,which can decompose complex signals in-to several band-limited intrinsic modal functions(IMFs).The core idea of VMD is to decompose the signal by solving a con-strained optimization problem,and optimize the decomposition process of the signal by using variational framework,so that each modal function has a limited bandwidth,thus realizing effective processing of non-stationary and nonlinear signals.The performance of the variational modal technique depends largely on the modal number K and the penalty factor α.Based on this,this paper optimizes the key parameters of VMD from the perspective of swarm intelligence optimization algorithm,takes the red-billed blue magpie optimization(RBMO)algorithm as the basic algorithm,and introduces the random barycenter reverse learning strategy to improve it,which greatly enhances the performance of the original algorithm and improves the convergence speed and accuracy of the original algorithm.The improved RBMO algorithm is used to optimize the key parameters of VMD,and the original signal image,objective function image and K natural modal functions are analyzed and compared in detail.