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Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter
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作者 PENG Pai CHEN Cong YANG Yongsheng 《Journal of Shanghai Jiaotong university(Science)》 EI 2020年第6期681-688,共8页
The combination of particle swarm and filters is a hot topic in the research of particle swarm op-timization(PSO).The Kalman filter based PSO(K-PSO)algorithm is efficient,but it is prone to premature convergence.In th... The combination of particle swarm and filters is a hot topic in the research of particle swarm op-timization(PSO).The Kalman filter based PSO(K-PSO)algorithm is efficient,but it is prone to premature convergence.In this paper,a particle filter based PSO(P-PSO)algorithm is proposed,which is a fine search with fewer premat ure problems.Unfortunately,the P-PSO algorithm is of higher computational complexity.In order to avoid the premature problem and reduce the computational burden,a hybrid Kalman filter and particle filter based particle swarm optimization(HKP-PSO)algorithm is proposed.The HKP-PSO algorithm combines the fast convergence feature of K-PSO and the consistent convergence performance of P-PSO to avoid premature convergence as well as high computational complexity.The simulation results demonstrate that the proposed HKP-PSO algorithm can achieve better optimal solution than other six PSO related algorithms. 展开更多
关键词 particleswarm opt imization(PSO) Kalman filter particle filter intelligent algorithm
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