基于ISPSO-VMD-MCKD的亚像元峰值提取方法  

Asian-Metal Peak Extraction Method Based on ISPSO-VMD-MCKD

作　　者：刘福康 杨光永[1] 吴大飞 徐天奇[1] LIU Fukang;YANG Guangyong;WU Dafei;XU Tianqi(School of Electrical Information Engineering,Yunnan Minzu University,Kunming 650000)

机构地区：[1]云南民族大学电气信息工程学院,昆明650000

出　　处：《计算机与数字工程》2022年第10期2135-2140,共6页Computer & Digital Engineering

基　　金：国家自然科学基金项目(编号:61761049,61261022)资助。

摘　　要：为解决标准粒子群算法(PSO)收敛速度慢和简化粒子群算法(SPSO)易陷入局部最优的问题,提出了迭代次数和粒子种群数相结合的动态更新种群学习率的策略方法,根据所提方法对粒子群算法和简化粒子群算法进行了改进调整。经仿真实验表明,改进粒子群算法(IPSO)算法相比于PSO算法收敛速度更快,改进简化粒子群算法(ISPSO)算法跳出局部最优的能力相比于SPSO算法更强。同时,将ISPSO算法与变分模态分解(VMD)算法和最大相关峭度反卷积(MCKD)算法相结合,提出了一种新的处理激光位移传感器信号方法(ISPSO-VMD-MCKD),并验证了算法的可行性及优越性。In order to solve the problem that the standard particle swarm algorithm(PSO)has a slow convergence speed,and the simplified particle swarm algorithm(SPSO)is easy to fall into the local optimum,a strategy method of dynamically updating the population learning rate combined with the number of iterations and the number of particle populations is proposed. The proposed method improves and adjusts particle swarm optimization and simplified particle swarm optimization. Simulation experiments show that the improved particle swarm optimization(IPSO)algorithm has faster convergence speed than the PSO algorithm,and the improved simplified particle swarm optimization(ISPSO)algorithm has stronger ability to jump out of the local optimum than the SPSO algorithm. At the same time,combining the ISPSO algorithm with the variational mode decomposition(VMD)algorithm and the maximum correlation kurtosis deconvolution(MCKD)algorithm,a new method for processing laser displacement sensor signals(ISPSO-VMD-MCKD)is proposed. And the feasibility and superiority of the algorithm are verified.

关 键 词：标准粒子群算法 简化粒子群算法 变分模态分解 最大相关峭度反卷积 

分 类 号：TN214[电子电信—物理电子学]