一类改进的粒子群优化算法对混沌系统未知参数的估计中期报告（Thisisamid-termreportontheestimationofunknownparametersinchaoticsystemsusinganimprovedparticleswarmoptimizationalgorithm）摘要：本文介绍了一种改进的粒子群优化算法，并将其应用于混沌系统的未知参数估计问题。在标准粒子群优化算法的基础上，本文提出了一种自适应权重策略，并结合多样性维护机制，以加快算法的收敛速度和提高算法的全局搜索能力。通过在多个已知混沌系统上的实验结果，本文验证了所提出算法的有效性和鲁棒性。关键词：粒子群优化；混沌系统；未知参数估计；自适应权重策略；多样性维护机制Abstract：Thispaperintroducesanimprovedparticleswarmoptimizationalgorithmandappliesittotheestimationofunknownparametersinchaoticsystems.Basedonthestandardparticleswarmoptimizationalgorithm,thispaperproposesanadaptiveweightstrategyandcombinesitwithadiversitymaintenancemechanismtospeedupconvergeandimproveglobalsearchability.Throughexperimentsonmultipleknownchaoticsystems,thispaperverifiestheeffectivenessandrobustnessoftheproposedalgorithm.Keywords:ParticleSwarmOptimization;ChaoticSystem;UnknownParameterEstimation;AdaptiveWeightStrategy;DiversityMaintenanceMechanism