CAJ | 학술논문

针对混合蛙跳算法(SFLA)更新策略会陷入局部最优、降低收敛速度的问题,提出一种自适应阈值更新策略.根据盲源分离中常用峭度和负熵作为非高斯性的度量,但峭度对野值敏感,影响算法性能,研究一种基于负熵准则的采用粒子群优化(PSO)算法和混合蛙跳算法的盲源分离方法.仿真结果表明,基于负熵的盲分离算法性能优于基于峭度的盲分离算法,基于SFLA的盲分离算法性能优于基于PSO的盲分离算法.
침대혼합와도산법(SFLA)경신책략회함입국부최우、강저수렴속도적문제,제출일충자괄응역치경신책략.근거맹원분리중상용초도화부적작위비고사성적도량,단초도대야치민감,영향산법성능,연구일충기우부적준칙적채용입자군우화(PSO)산법화혼합와도산법적맹원분리방법.방진결과표명,기우부적적맹분리산법성능우우기우초도적맹분리산법,기우SFLA적맹분리산법성능우우기우PSO적맹분리산법.
Aiming at the problem of slowing the convergence and facing local optimization of Shuffled Frog Leaping Algorithm(SFLA) update strategy,this paper proposes a update strategy of adaptive threshold selection is raised.Kurtosis and negative entropy are used as a measure of non-Gaussian in Blind Source Separation(BSS),but kurtosis is sensitive to outliers affecting performance of BSS,it researches a criteria of negative entropy based on Particle Swarm Optimization(PSO) algorithm and SFLA.Simulation results show that the proposed BSS of negative entropy has significant performance improvement over BSS of Kurtosis and BSS based on SFLA has better performance over BSS based on PSO.