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pso algorithm     
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  pso算法
    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
    Initialization of the wavelet parameters and the applications of advanced PSO algorithm in WNN
    WNN中的改进PSO算法及参数初始化
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    Application of PSO Algorithm with Compression Factor in Gear-box Faults Diagnosis
    带收缩因子的PSO算法在齿轮箱故障诊断中的应用
短句来源
    In the system a kind of controller is designed which is the combination of PSO algorithm and PID control theory.
    并将PSO算法与PID控制方法相结合,设计了一种基于PSO算法的PID控制器;
短句来源
    This scheme expand the application range of neural network and PSO algorithm in
    同时也拓展了神经网络和PSO算法等智能算法在工业控制领域的应用范围,在一定程度上推动了神经网络技术、PSO算法等智能算法的发展。
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  粒子群算法
    Application of PSO Algorithm in Wavelet Neural Network
    粒子群算法在小波神经网络中的应用
短句来源
    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
    For general nonlinear optimization with constraints, a neural network model and a revised PSO algorithm are proposed.
    针对一般的带有等式约束和不等式约束的非线性优化问题,提出了神经动力学求解模型,对多模的非线性优化问题改进了粒子群算法以获得更好近似全局最优解。
短句来源
    A new intelligent algorithm , particle swarm optimization (PSO) algorithm, is adopted to solve the reactive power optimization. Optimization results in PSO and GA algorithm are compared to show the superiority of PSO algorithm in this problem.
    本文介绍了一种新的智能算法――粒子群算法,并用它来解决电力系统无功优化问题,所得结果与基于遗传算法的优化结果进行比较,论证了粒子群算法在解决这一问题上的优越性。
短句来源
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  微粒群算法
    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
    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
    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。
短句来源
    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. *
短句来源
    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.
短句来源
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  pso算法的
    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
    Optimal design for two degree-of-freedom PID controller based on PSO algorithm
    基于PSO算法的目标值前馈型二自由度PID控制器的优化设计
短句来源
    Study on Function Optimization and Model Reduction Based on a Class of Hybrid PSO Algorithm
    基于一类混合PSO算法的函数优化与模型降阶研究
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    A nonlinear model predictive control based on PSO algorithm
    一种基于PSO算法的非线性模型预测控制方法
短句来源
    A FCMAC Controller Based on PSO Algorithm and its Application for Fresh Steam Temperature Control
    基于PSO算法的FCMAC控制器及其在主汽温控制中的应用
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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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