%0 Journal Article
%T 具有适应性突变和惯性权重的粒子群优化(PSO)算法及其在动态系统参数估计中的应用
%A ALFI
%A Alireza
%J 自动化学报
%P 541-549
%D 2011
%R 10.3724/SP.J.1004.2011.00541
%X ？Animportantprobleminengineeringistheunknownparametersestimationinnonlinearsystems.Inthispaper,anoveladaptiveparticleswarmoptimization(APSO)methodisproposedtosolvethisproblem.Thisworkconsiderstwonewaspects,namelyanadaptivemutationmechanismandadynamicinertiaweightintotheconventionalparticleswarmoptimization(PSO)method.Thesemechanismsareemployedtoenhanceglobalsearchabilityandtoincreaseaccuracy.First,threewell-knownbenchmarkfunctionsnamelyGriewank,RosenbrockandRastrigrinareutilizedtotesttheabilityofasearchalgorithmforidentifyingtheglobaloptimum.TheperformanceoftheproposedAPSOiscomparedwithadvancedalgorithmssuchasanonlinearlydecreasingweightPSO(NDWPSO)andareal-codedgeneticalgorithm(GA),intermsofparameteraccuracyandconvergencespeed.ItisconfirmedthattheproposedAPSOismoresuccessfulthanotheraforementionedalgorithms.Finally,thefeasibilityofthisalgorithmisdemonstratedthroughestimatingtheparametersoftwokindsofhighlynonlinearsystemsasthecasestudies.
%K Particleswarmoptimization(PSO)
%K parameterestimation
%K nonlineardynamics
%K inertiaweight
%K adaptivemutation
%U http://www.aas.net.cn/CN/abstract/abstract17479.shtml