Particle swarm Optimization (PSO) algorithm is based on swarm intelligence theory.
粒子群优化(PSO)算法是基于群体智能理论的优化算法。
Particle Swarm Optimization (PSO) algorithm has existed premature convergence for multimodal search problems.
粒子群优化(PSO)算法对于多峰搜索问题一直存在早熟收敛问题。
Considering that the particle swarm optimization (PSO) algorithm is quite simple and easy to implement, it was used to estimate the nonlinear model parameters in this paper.
粒子群算法操作简便、容易实现且全局搜索功能较强，适用于非线性参数估计。
Particle Swarm Optimization(PSO)algorithm is one of embranchments of swarm intelligence.
粒子群优化算法是群体智能中一个新的分支。
To gain optimization parameters of hydro turbine PID governor, this paper interprets the approach of optimization designing that uses the Particle Swarm Optimization (PSO) algorithm.
为了保证获得最优水轮机PID调节器参数，本文研究了利用微粒群优化（PSO）算法进行参数优化设计的新方法。
The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain.
经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的很有效的方法。
An algorithm for discretization based on Particle swarm optimization (PSO) is presented, which can settle the problem of continuous attributes discretization in systema modeling perfectly.
提出了一种基于微粒群优化(PSO)算法的连续属性离散化方法，很好的解决了建模过程中连续属性的离散化问题。
An improved particle swarm optimization (PSO) algorithm was designed. And a weighted ITAE index of turbine speed error was taken as the fitness function of the improved PSO algorithm.
提出了一种新的改进的粒子群优化算法，并以水轮机转速偏差的加权ITAE指标作为改进粒子群优化算法的适应度函数。
Based on the swarm intelligence, Particle swarm optimization (PSO) algorithm is a kind of modern optimization method inspired by the research of the artificial life.
粒子群算法是基于群集智能、受到人工生命研究结果的启发而提出的一种现代优化方法。
Aimed at particle swarm optimization (PSO) algorithm being easily trapped into local minima value in multimodal function, a rotating surface transformation (RST) method was proposed.
针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值，提出旋转曲面变换(RST)方法。
The Particle Swarm Optimization (PSO) algorithm was used to deal with task assignment problem in multi-suppliers' participation in collaborative product development.
通过粒子群最优化算法解决多供应商参与协同产品开发时的任务指派问题。
A new algorithm of swarm intelligence, Particle swarm Optimization (PSO), which is an algorithm of simple implementation and fast convergence with few parameters, is introduced in this paper.
介绍了一种新的集群智能算法-微粒群算法(PSO)，该算法具有实现简单、参数少且收敛快的特点。
Particle Swarm Optimization (PSO) algorithm for solving multiple Nash equilibrium solutions of bimatrix game is presented in this paper.
提出了一种求解双矩阵对策多重纳什均衡解的粒子群优化算法。
Particle Swarm Optimization (PSO) algorithm is a powerful method to find the extremum of a continuous numerical function.
微粒群优化算法是求解连续函数极值的一个有效方法。
This paper incorporates chaos optimization algorithm into the PSO algorithm, and propose a new particle swarm optimization algorithm based on chaos searching (CPSO).
文章把混沌优化搜索技术引入到P SO算法中，提出了基于混沌搜索的粒子群优化算法。
A novel hybrid algorithm approach that employs a particle swarm optimization (PSO) and a multistage detection for the multiuser detection problem (PSOMSD) is proposed.
提出了一种新颖的基于粒子群优化和多级检测的混合算法的多用户检测器。
To optimize the damage probability, the particle swarm optimization algorithm (PSO) is adopted to optimize the system parameters, based on above models.
以上述模型为基础，以毁伤概率为优化目标，采用微粒群算法（PSO）对该系统待优化的各参量进行综合优化。
A discrete Particle Swarm Optimization (PSO) algorithm was presented for Job Shop scheduling problem.
提出了用于解决作业车间调度问题的离散版粒子群算法。
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 - P SO)。
In allusion to the problem of dynamic self-calibration, a novel self-calibrating algorithm for visual position based on particle swarm optimization (PSO) is suggested in this paper.
针对动态自标定的问题，提出了一种改进的基于粒子群优化(PSO)的自标定位置视觉定位算法。
A new adaptive filtering model based on particle swarm optimization (PSO) algorithm is proposed and designed. It is proved to be efficient and effective in the computer simulation example test.
提出并设计了一种基于粒子群优化算法的振动信号的自适应滤波模型。该滤波模型在计算机仿真测试中，获得了很高的效率和良好的结果。
To avoid the shortcomings of FCM and Particle Swarm Optimization algorithm, new hybrid clustering algorithm based on PSO and FCM algorithm is proposed.
针对模糊c均值算法与粒子群算法的不足，提出了一种基于粒子群算法和模糊c—均值算法的混合聚类算法。
Particle Swarm optimization (PSO) algorithm is a population-based global optimization algorithm, but it is easy to be trapped into local minima in optimizing multimodal function.
粒子群优化算法应用于多极值点函数优化时，存在陷入局部极小点和搜寻效率低的问题。
The chaos search based hybrid particle swarm optimization (PSO) algorithm is proposed in the paper to avoid the premature phenomenon of PSO, which is applied into the reactive power optimization.
应用粒子群优化算法(PSO)求解电力系统无功优化问题，提出基于混沌搜索的混合粒子群优化算法，以克服P SO容易早熟而陷入局部最优解的缺点。
Specific particle swarm optimization (PSO) algorithm is designed to solve the model.
针对模型的特点设计了求解模型的特殊PSO算法。
Based on PSO, a new PID control method with incomplete derivation based on particle swarm optimization algorithm is proposed.
以P SO算法为基础，提出了一种新的粒子群优化不完全微分pid算法。
As a representative swarm-intelligence based optimization algorithm, Particle SwarmOptimization (PSO) algorithm is applied to capacitor optimization in the dissertation.
微粒群优化算法（PSO）是目前备受关注的群集智能算法的代表性方法，也是本文研究工作的算法基础。
A concrete dam deformation forecasting model is established based on the particle swarm optimization (PSO) algorithm and the traditional multi-statistical regression model.
将粒子群算法引入大坝安全监控领域，并结合多元回归统计模型，建立基于粒子群算法的混凝土坝变形预报模型。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
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