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dc.contributor.author郭振雄
dc.contributor.author陈玉叶
dc.contributor.author肖可
dc.contributor.author何俊杰
dc.contributor.author刘畅
dc.contributor.author潘书万
dc.contributor.author陈松岩
dc.date.accessioned2018-11-26T08:50:35Z
dc.date.available2018-11-26T08:50:35Z
dc.date.issued2017
dc.identifier.citation厦门大学学报. 自然科学版,2017,(5):704-710
dc.identifier.issn0438-0479
dc.identifier.other10.6043/j.issn.0438-0479.201702023
dc.identifier.urihttps://dspace.xmu.edu.cn/handle/2288/165541
dc.description.abstract在优化非线性复杂系统问题中,智能算法已成为一种重要手段.提出了一种基于动态感知系数的自适应粒子群优化(particle swarm; optimization,PSO)算法(self-tuning; PSO,SPSO),将PSO算法的感知系数与神经网络算法结合,并于在线学习训练过程中动态调整感知系数,改善了PSO算法的计算效率以及全局收敛效率; .进一步将2个相互关联的神经网络比例积分微分(proportion integration; differentiation,PID)神经网络及SPSO神经网络结合起来,使其能有效解决非线性控制模型的问题.为了验证该算法,引入了4个仿真例; 及2种PSO优化算法传统PSO(conventional PSO,CPSO)和修正PSO(modified; PSO,MPSO),来比较SPSO算法在解决控制问题中的非线性复杂系统的高效性,结果显示SPSO算法有较好的全局收敛性能、收敛速度以及较强的鲁棒; 性.
dc.description.abstractWe propose a dynamic perception-coefficients algorithm,called; self-tuning particle swarm optimization (SPSO) in this paper, which is; used for determining optimal nonlinear systems.The theory of SPSO; integrates conventional PSO (CPSO) with neural network (NN) and trains; perception coefficients with the gradient descent algorithm to improve; computational efficiency,rate of convergence and global convergence.Then; we combine proportional-integral-derivative (PID) NN with SPSO to; optimize complex nonlinear systems.This paper presents four examples and; another two optimization algorithms CPSO modified PSO(MPSO) to help; estimate performances of the proposed SPSO.The result demonstrates that; the SPSO exhibits great performances in global convergence, rate of; convergence and strong robustness while optimizing complex nonlinear; systems.
dc.description.sponsorship福建省科技计划项目; 泉州市科技计划项目
dc.language.isozh_CN
dc.subject优化算法
dc.subject自适应粒子群优化
dc.subject动态感知系数
dc.subject比例积分微分
dc.subject神经网络
dc.subject自调节非线性系统
dc.subjectoptimization algorithm
dc.subjectself-tuning particle swarm optimization (SPSO)
dc.subjectdynamic perception coefficients
dc.subjectproportional-integral-derivative (PID)
dc.subjectneural network(NN)
dc.subjectself-tuning nonlinear system
dc.title一种基于非线性系统的动态感知系数的自适应粒子群优化算法
dc.title.alternativeA Dynamic Perception Coefficients Self-tuning Particle Swarm Optimization Algorithm Based on Nonlinear Systems
dc.typeArticle


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