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微粒群算法
    Optimization Design of Hydro Turbine Governor Parameters by PSO Algorithm
    用微粒群算法实现水轮机调节器参数的优化设计
    Application of PSO algorithm in design of automated control system
    微粒群算法在自动控制系统设计中的应用
    A Hybrid PSO Algorithm for Flow Shop Scheduling Problem with Earliness/Tardiness
    基于提前/滞后Flow Shop调度问题的混合微粒群算法
    Modified PSO Algorithm Solving Flow-shop Scheduling Problem with Fuzzy Delivery Time
    改进微粒群算法求解模糊交货期Flow-shop调度问题
    Niche PSO algorithm based on CA
    基于元胞自动机的小生境微粒群算法
    A Study of Evolving Classification Rule Based on PSO Algorithm
    基于微粒群算法生成分类规则
    First, the model of the controlled object was estimated using PSO algorithm based on the experiment data, and then the PSO algorithm was proposed to design the parameter optimization of controller.
    系统设计过程包括两个部分首先基于历史输入输出数据,用微粒群算法建立系统的模型,然后基于得到的模型进行控制器的设计,并用微粒群算法进行控制器的参数优化整定。
    A multi-modality function optimization based on PSO algorithm
    多峰函数寻优的微粒群算法
    On the basis of systematical summary of PSO algorithm and its application, the article mainly proceeding 0-1 PSO model research on and applying it to the system of choosing experts optimization.
    本文在对该算法及其应用进行全面综述的基础上,重点进行了离散二进制PSO算法的研究,并将微粒群算法应用于专家选择系统中。
    The experimental results show that the PSO algorithm provides an effective method to estimate parameters of NSM.
    实验结果表明:微粒群算法为非线性系统模型参数估计提供了一种有效的途径. *
    A 'density based' clustering algorithm is used in PSO to construct a modified niching PSO algorithm.
    在微粒群算法中引入“基于密度”的聚类算法,构建出一种改进的小生境微粒群算法.
    First,PSO algorithm was introduced. Then,on the basis of the critical parameters analysis, the research progress on PSO algorithm was summarized in such as- pects as discrete binary expression, parameter selection and design, organization and evolution of popula- tion, hybrid PSO algorithm etc.
    本文首先介绍微粒群算法,然后在对算法的关键参数进行分析的基础上,从算法的离散二进制表示、参数选择与设计、群体组织与进化以及混合微粒群算法等方面对国内外微粒群算法的研究进展进行综述。
    When parameters of hydro turbine PID governor are optimized by using PSO algorithm, the experiments show excellent results in term of control accuracy and effectiveness.
    仿真试验结果表明,用微粒群算法优化水轮机调节器参数,可以获得满意的控制精度和效率.
    On the foundation of analysing every parameter of PSO algorithms,PSO algorithm is reformed and particles move from initialization position along multimodal function super-surface toward local optimization direction until reach nearest local optimization.
    在分析微粒群优化算法中各个参数对微粒运动影响的基础上,对微粒群算法进行改造,让微粒运动从初始位置沿优化函数曲线向优化峰值方向爬行,直至找到所在区域的局部优化峰值;
    According to the flow shop scheduling problem with fuzzy delivery time,this paper uses particle swarm optimization(PSO) to solve this problem,which has good convergence speed and performance in searching global optimum. The modified PSO algorithm can immune to the local extremum by using some methods of penalty function,memory database,self-adaptive mutation and so on.
    针对模糊交货期Flow-shop调度问题的特点,论文提出用微粒群这种具有快速收敛、全局性能好的迭代优化算法进行求解,并使用惩罚函数、增加数据记忆库和自适应变异机制等方法对微粒群算法进行改进,减少了算法陷入局部极值的可能性。
    The adoption of niche concept improves the ability of PSO algorithm in solving multimodel function optimization problems.
    小生境技术的引入,提高了微粒群算法处理多峰函数优化问题的能力。
    In this paper,we present a new niche PSO algorithm which is based on CA neighborhood,and is tested through typical function,the simulation result shows that the algorithm is more effective in complicated function optimization than normal niche PSO algorithm based on circular structure.
    提出了基于元胞自动机邻域的小生境微粒群算法,并通过对典型函数进行测试,实验结果表明,该算法在复杂函数(较难收敛函数)的优化中比基于环形结构的标准小生境微粒群算法要好。
    But the standard PSO can't lead to convergence of global optimization. In this paper, the improved PSO algorithm, called stochastic PSO, is used to guarantee converge to the global solution for parameter optimization of PID controller.
    但基本微粒群算法不能保证全局收敛,本文将改进的PSO算法(SPSO)应用于PID控制器的参数优化.
    A new optimazation algorithm—Particle swar(?) optimization (PSO) is introduced. The performance of two sets of PSO algorithm,a fixed inertia weight one and a linear decreasing inertia weight one,is studied.
    介绍了一种新的智能优化算法——微粒群算法(PSO),并研究了两种 PSO 算法的性能:固定惯性权重的 PSO 和线性时变惯性权重的 PSO,探讨了参数选择的影响,并对5个 benchmark 问题进行了测试。
 

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