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pso algorithm     
相关语句
  pso算法
     Using the PSO algorithm to optimize the on-line PID controller's parameters, desirable control effect is obtained.
     通过运用PSO算法对PID控制器参数进行在线调整,使模型参考自适应控制达到理想的控制效果。
短句来源
     Application of Modified PSO Algorithm in the Optimization of Reactive Power
     改进PSO算法在无功优化中的应用
短句来源
     Rotate surface transformation PSO algorithm for solving (nonlinear) (optimal) control problem
     旋转曲面变换PSO算法解非线性最优控制问题
短句来源
     Comparison studies between the optimized data obtained by genetic algorithm(GA) and those by PSO algorithm were made.
     同时对遗传算法(GA)和PSO算法优化结果进行对比研究.
短句来源
     To avoids the local minimum problems and to improve convergent speed, a new probability of PSO algorithm was proposed.
     为了避免PSO算法在求解最优化问题时陷入在局部最优及提高PSO算法的收敛速度,提出了对PSO算法增加更新概率。
短句来源
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  粒子群算法
     A Study of Application of An Improved PSO Algorithm in BP Network
     一种改进的粒子群算法在BP网络中的应用研究
短句来源
     To improve the searching performance of Particle Swarm Optimization (PSO), a modified PSO algorithm with flying time adaptively adjusted was proposed and named FAA-PSO algorithm.
     为改善粒子群优化算法的搜索性能,提出一种飞行时间自适应调整的粒子群算法(FAA-PSO)。
短句来源
     The experiment results show that the PSO algorithm has better capability for identifying nonlinear system and better generalization ability than the BP algorithm.
     仿真实验结果表明,粒子群算法具有比BP算法更强的非线性系统辨识能力和更好的泛化能力。
短句来源
     PSO Algorithm Based Optimal Supply Function Model for Power Producer
     基于粒子群算法求解电力市场发电商最优供给函数模型
短句来源
     Improved PSO Algorithm Study and Its Application on Network Routing
     改进粒子群算法研究及其在网络路由中的应用
短句来源
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  微粒群算法
     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。
短句来源
     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
短句来源
     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
短句来源
     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
短句来源
     A Study of Evolving Classification Rule Based on PSO Algorithm
     基于微粒群算法生成分类规则
短句来源
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  pso算法的
     Performance analysis of adaptive PMD compensation in WDM system based on PSO algorithm
     基于PSO算法的WDM系统中自适应PMD补偿的性能分析
短句来源
     PID Parameters Optimization for Motor Controller System Based on Improved PSO Algorithm
     基于改进PSO算法的电机控制系统PID参数优化
短句来源
     Neural Network Ensembles Based on Improved PSO Algorithm
     基于改进的PSO算法的神经网络集成
短句来源
     Image segmentation based on PSO algorithm
     基于PSO算法的图像分割方法
短句来源
     STRUCTURAL DAMAGE DETECTION BASED ON AN IMPROVED PSO ALGORITHM
     基于改进PSO算法的结构损伤检测
短句来源
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      pso algorithm
    The ability of tracking changed PMD using PSO algorithm was also investigated.
          
    The Particle Swarm Optimization (PSO) algorithm was introduced in adaptive PMD compensation.
          
    Two-stage adaptive PMD compensation in 40 Gb/s OTDM optical communication system using PSO algorithm
          
    Subsequently the Particle Swarm Optimization (PSO) algorithm is applied in order to select the "the best" ANN for the future investment decisions and to adapt the weights of other networks towards the weights of the best network.
          
    The new methods are based on the hierarchical PSO (H-PSO) and a new type of H-PSO algorithm, called Partitioned Hierarchical PSO (PH-PSO).
          
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    Particle swarm optimization(PSO)is an efficient stochastic global optimization technique making use of a particle population,where each particle represents a solution to the problem being optimized.The particle swarm algorithms find optimal regions of complex search spaces through the interaction of individuals in a population of particles.A novel variant of original PSO algorithm is proposed in this paper.An item of integral control and a constraint of position are added to the traditional...

    Particle swarm optimization(PSO)is an efficient stochastic global optimization technique making use of a particle population,where each particle represents a solution to the problem being optimized.The particle swarm algorithms find optimal regions of complex search spaces through the interaction of individuals in a population of particles.A novel variant of original PSO algorithm is proposed in this paper.An item of integral control and a constraint of position are added to the traditional position update rule in order to improve performance of original PSO algorithm.The experiment results demonstrate that proposed algorithm is superior to original PSO algorithm and modified PSO algorithm with inertia weight reduced linearly.

    粒子群优化(PSO)是一类有效的随机全局优化技术。它利用一个粒子群搜索解空间,每个粒子表示一个被优化问题的解,通过粒子间的相互作用发现复杂搜索空间中的最优区域。提出一类新颖的PSO算法,该算法在基本PSO算法的粒子位置更新公式中增加了一个积分控制项。积分控制项根据每个粒子的适应值决定粒子位置的变化,改善了PSO算法摆脱局部极小点的能力。另外,该算法增加了限制搜索空间范围的机制,这对某些函数优化问题是必需的。用5个基准函数做的对比实验结果显示,该算法优于基本PSO算法以及自适应修改惯性因子的PSO算法。

    Due to the simplicity and the prominent effect, particle swarm optimization(PSO) algorithm is applied in model optimization more and more frequently and it was used to optimize the mathematical model for the width expansion. The result from the computer simulation demonstrated the algorithm's validity and effectiveness. 

    粒子群优化算法因为其简单可行以及效果显著而越来越广泛地被应用于模型优化中,采用粒子群算法对粗轧宽展控制模型进行优化,解决了传统方法难以解决的问题,结果表明优化后的模型效果明显优于原来模型,体现了粒子群算法在优化领域的优越性。

    As a representative method of swarm intelligence, particle swarm optimization (PSO) is an algorithm for searching the multidimensional complex space efficiently through cooperation and competition among the individuals in a population of particles. A modified PSO (MPSO) algorithm is proposed. The MPSO employs local version constriction factor method and global version inertia weight method simultaneously to achieve relatively high performance. To avoid the possible occurring of stagnation...

    As a representative method of swarm intelligence, particle swarm optimization (PSO) is an algorithm for searching the multidimensional complex space efficiently through cooperation and competition among the individuals in a population of particles. A modified PSO (MPSO) algorithm is proposed. The MPSO employs local version constriction factor method and global version inertia weight method simultaneously to achieve relatively high performance. To avoid the possible occurring of stagnation phenomenon in the PSO algorithm, the re-initialization mechanism based on the global information feedback is introduced in the MPSO. Numerical examples show the effectiveness of the proposed algorithm.

    作为群集智能的代表性方法之一,粒子群优化(PSO)算法通过粒子之间的合作与竞争以实现对多维复杂空间的高效搜索。提出了一种改进粒子群优化(MPSO)算法。MPSO同时采用局部模式压缩因子方法和全局模式惯性权重方法以获得相对较高的性能。针对PSO算法可能出现的停滞现象,MPSO引入了基于全局信息反馈的重新初始化机制。数值仿真结果显示了该算法的有效性。

     
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