PSO
美 
英
- 网络粒子群优化算法(Particle Swarm Optimization)；粒子群算法；微粒群算法
例句
The experiment results showed that this approach has advantages of both PSO and FLANN, meanwhile it has better precision.
实验结果表明，该方法结合了PSO和FLANN两者的优点，建模精度高。
Although very easy to implement, this hybrid SM-PSO is an efficient way to locate global optima of continuous multimodal functions.
算法实现简单，具有很高的可靠性，是一种求解多峰连续函数极值的有效方法。
It used to solve the problem that Particle Swarm Optimization(PSO) easily falls into a local extremum.
克服了经典粒子群算法中参数选择问题以及粒子群算法易陷入局部极值问题。
this document is mainly targeted at PSO algorithm in Matlab environment application to the preparation of that document m!
本文件主要是针对于粒子群算法在matlab环境中地应用说编写地m文件！
And, in FNN weight training, improved PSO in the convergence rate and the ability to jump out to local optimum algorithm is better than BP.
且改进的粒子群算法在模糊神经网络权值的训练中收敛速度和跳出局部最优的能力都要比BP算法更优。
To perform the parallel optimization, the comparison used in tournament selection was employed to compare the searched solutions of the PSO.
采用联赛选择算子比较粒子群算法所搜索到的解。
Task assignment problem is a typical NP problem. Particle Swarm Optimization (PSO) algorithm was used to solve task assignment problem.
任务指派问题是典型NP难题，引入粒子群优化算法对其进行求解。
Finally, this optimization algorithm is proved to be effective by an example.
结合一个仿真算例，表明了采用基于PSO的军械调运决策优化算法的有效性。
To this problem, this paper proposed one kind of method to choose the parameters of the SVM by particle swarm optimization algorithm (PSO).
针对此问题，提出一种基于粒子群优化算法的支持向量机参数选择方法。
This variant of PSO enables the diversity of the swarm to be preserved to discourage premature convergence.
由于该方法能够保持群体的多样性，因此可以避免早熟收敛。
This new algorithm applies particle swarm optimization (PSO) to design the CNN templates to identify the edge of a nucleated cell.
该方法运用粒子群优化算法设计CNN模板，利用CNN对骨髓有核细胞进行边缘检测。
The PSO attribute reduction algorithm ought to improve the efficiency, however, it has the premature convergence problem.
粒子群（PSO）属性约简算法，虽然可提高求解效率，但易陷入局部最优。
Subsequently, this paper focuses on PSO optimization model and its applications in RFID Tags and Antenna Simulation Deployment System.
随后，本文重点介绍了粒子群算法模型及其在RFID标签天线仿真部署系统中的应用。
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。
It transformed the parameter selection problem into functional optimization problem by creating a function of the PSO property parameters.
针对特定问题，将PSO方法的性能表示成参数的函数，从而将参数选择问题转变成函数优化问题。
Uses multi- populations cooperation optimization algorithm which PSO develops, good article. May have a look to use.
详细说明：使用PSO开发的多种群协作优化算法，好文章啊。可以看看采用啊。
Particle swarm optimization (PSO) is a good inversion method with characterizing simple algorithm, fast convergence, and easy operation.
粒子群优化算法是一种很好的优化反演方法，具有算法简单、收敛较快、容易实现等特点；
Another is to examine faults of asynchronous Motors in terms of BP neural network based on Particle Swarm Optimization(PSO).
二是利用基于粒子群算法（PSO）优化的BP神经网络进行异步电机故障诊断。
The simulation results show that the improved PSO algorithm can solve the high-dimensional numerical optimization problem effectively.
实验结果表明该改进微粒群算法可以有效地解决高维数值优化问题。
The computational results show that the PSO is a viable and effective approach to solve the semiconductor furnace batch scheduling problem.
实例计算的结果表明，该算法是解决半导体炉管区调度问题可行且高效的方法。
Aiming to solve out the pre-mature convergence phenomenon, the chaos mutation is introduced into PSO for improving global optimal ability.
混沌变异机制引入到PSO算法中，克服了进化过程中出现的早熟收敛现象，改进了PSO算法的全局寻优能力；
Then, the problem was solved by particle swarm optimization algorithm (PSO) after elimination of the constraint equations.
然后针对分解后的子问题，利用微粒群优化算法（PSO）求解。
Results show that the improved PSO algorithm has a great improve in global search capability and solution precision.
实验表明改善后的算法的求解精度和全局搜索能力得到较大的提高。
The theoretical analysis and experimental results indicate that the proposed niche PSO algorithm is feasible and effective.
理论分析及实验结果表明，该算法是有效可行的。
Summary of Background Data. PSO is a technique popularized in the lumbar spine primarily for the correction of fixed sagittal imbalance.
研究背景概述：PSO是一种广泛用于腰椎矢状面失衡矫正的主要技术。
Application examples show that it is feasible to apply the improved PSO to the weight solution of power load combination forecasting model.
通过应用实例证明，将改进的粒子群优化算法应用到电力负荷组合预测模型的权重求解是可行的。
At last, individual decision-making PSO is applied into solving nonlinear equations problem, simulation results show they are more superior.
最后把个体决策微粒群算法应用到非线性方程组求解问题中，仿真结果表明它们具有较大的优势。
By adopting a dynamic inertia gene and condensed network structure, this article improve BP-PSO algorithm, efficiently solve these problems.
因此，本文采用一种动态惯性因子并精简网络结构的改进BP-PSO算法，有效解决这些问题。
This paper adds mutation operator to adaptive PSO and apply it in the lymphoma morphology parameter classifier problems.
对自适应粒子群算法引入变异算子，并对其进行改进，将其应用到淋巴瘤形态参数的分类问题上。
Particle swarm optimization (PSO) algorithm is easy to be trapped into local minima in optimizing multimodal function.
针对利用粒子群优化算法进行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。
PSO clustering algorithm is known to have simple parameters and fast convergence, but there are also local optimal problems.
粒子群优化聚类算法具有参数简单，收敛快等优势，但也有局部极值问题。
Considering the stronger search ability of Particle Swarm Optimization(PSO), this paper introduces a PSO covering algorithm.
为此，结合粒子群优化（PSO）具有的全局搜索能力，提出一种PSO覆盖算法。
During the search process of PSO, the chaotic local optimizer was introduced to raise its resulting precision and convergence rate.
然后，在PSO的搜索过程中引入混沌局部搜索策略，来提高解的精度和收效速度。
Adaptive perceptive ability is assigned to particles in PSO for balancing their global and local searching and avoiding prematurity.
通过为粒子赋予自适应感知能力，算法能较好地平衡全局和局部搜索，且有能力跳出局部极值，防止早熟。
The parameters and thresholds of classifiers are optimized by improved Particle Swarm Optimization(PSO) algorithm.
改进的粒子群优化算法全局搜索BP神经网络的权值和阈值。
Finally, we use an engineering example with PSO arithmetic to realize optimization of the machine tool basic shaft.
最后根据机床主轴优化设计的实例采用PSO算法，实现了对机床主轴结构的优化设计。
If some particles trended to local extremum in PSO algorithm implementation, the particle velocity was updated and re-initialized.
在PSO算法的运行过程中，对有集聚倾向的粒子进行速度变异处理，重新初始化速度。
Particle Swarm Optimization (PSO for short) is a evolutionary algorithm with simple operations and few parameters.
粒子群优化算法（PSO）是一种进化算法，操作简单，参数少。
Emulation experiments demonstrated that the modified algorithm improves the PSO's global search capability remarkablely.
仿真实验表明，改进的粒子群算法显著提高了PSO算法的全局搜索能力。
This paper presents a survey of the PSO on Project of Power Grid Construction and Irrigation, and provides some outlook.
本文分析了目前应用于电网构建和农田水利灌溉等工程中的微粒群算法，并提出了一些展望。
