qpso

Prediction of Chaotic Time Series Based on QPSO : RBF NN

基于QPSO&RBFNN的混沌时间序列预测

The purpose of the paper is to research and improve Quantum-behaved Particle Swarm Optimization ( QPSO ) algorithm .

本文的目的是研究和改进具有量子行为的粒子群算法（Quantum-behavedparticleswarmoptimization，简称QPSO）。

An improved Quantum-behaved Particle Swarm Optimization ( QPSO ) for multi-peaks functions optimization was proposed .

介绍了一种利用量子行为粒子群算法（QPSO）求解多峰函数优化问题的方法。

So the Quantum-behaved PSO ( QPSO ) algorithm which has power global searching ability than PSO is introduced for improving in this paper .

因此在本文中引入了具有着更强全局搜索能力的QPSO算法来进行研究改进。

This paper introduces a bounded mutation operator into Quantum-behaved Particle Swarm Optimization ( QPSO ) algorithm and proposes QPSOB .

将边界变异操作引入到量子粒子群优化算法中，提出基于边界变异的量子粒子群优化算法（QPSO）B。

Because of the use of QPSO , the fused image is more relative with the multi-spectral and panchromatic imagery .

由于采用了QPSO算法，使最后变换后的图像与多光谱图像和全色图像都有很强的相关性。

Take a certain district as an example , we have applied Quantum Particle Swarms Optimization ( QPSO ) on DG optimization and drawn a detailed analysis on the result .

以某一地区作为实例，应用量子粒子群算法对分布式电源接入进行优化配置，并且对优化结果进行详细分析。

From the view of quantum mechanics , Sun et al . proposed the quantum-behaved particle swarm optimization ( QPSO ) via the research on particle convergence behaviour .

Sun等人从量子力学的角度，通过对粒子收敛行为的研究，基于PSO算法提出了量子粒子群算法（Quantum-behavedparticleswarmoptimization，QPSO）。

Quantum-behaved Particle Swarm Optimization ( QPSO ), is a new type , efficient swarm intelligence algorithm that proposed lately succeed to Particle Swarm Optimization ( PSO ) .

具有量子行为的粒子群优化（Quantum-behavedparticleswarmoptimization，QPSO）算法是继粒子群优化算法（ParticleSwarmoptimization，PSO）后，最新提出的一种新型、高效的进化算法。

By simulation of IEEE-30 node and IEEE-14 node , comparing them with PSO and GA algorithm , the result shows QPSO can do better in global optimal solution .

通过对IEEE-30节点和IEEE-14节点系统进行仿真计算，并与PSO算法、GA算法进行比较，表明该算法能更好地获得全局最优解。

One is the Quantum-Behaved Particle Swarm Optimization ( QPSO ), the other is the particle swarm optimization with maximal velocity contractile strategy ( MVCS-PSO ) .

一种是量子行为的粒子群优化算法（QPSO），另一种为最大速度收缩策略的粒子群优化算法（MVCS-PSO）。

Not many parameters of QPSO need to be adjusted and the randomicity of QPSO is strong , so QPSO can guarantee the efficiency and global convergence of algorithm .

QPSO没有过多参数需要调整，随机性强，能够保证算法的高效性和全局收敛性。

Genetic Algorithm ( GA ) and Particle Swarm Optimization ( PSO ) were tested for performance comparison with QPSO , and the result showed the good efficiency of QPSO algorithms to image fusion .

与PSO算法和遗传算法进行了比较，证明了QPSO算法在图像融合中具有良好的效果。

Quantum-behaved PSO ( QPSO ) algorithm is a modification of the standard PSO algorithm , and it has fewer parameters to control and can be demonstrated mathematically to be a global convergent algorithm .

量子行为粒子群优化算法是对标准PSO的一种改进，参数少，理论上能保证解全局收敛。

Quantum-behaved Particle Swarm Optimization ( QPSO ) is a novel PSO algorithm model in terms of quantum mechanics . The model is based on Delta potential and think the particle had the behavior of quanta .

量子粒子群优化QPSO（Quantum-behavedparticleswarmoptimization）算法是从量子力学的角度出发提出的一种新的PSO算法模型，这种模型以Delta势阱为基础，认为粒子具有量子的行为。

At present , some of the advanced intelligent algorithms are gradually introduced to the process control to tune the parameters of PID controller , for example , the quantum quantum-behaved particle swarm optimization ( QPSO ) .

而目前一些先进的智能算法正逐步引进过程控制中来整定PID参数，量子粒子群算法（QPSO）就是其中之一。

There are a variety of improved SVM algorithms , whose advantages and disadvantages exist simultaneously . On the basis of previous studies , quantum particle swarm optimization ( QPSO ) is introduced to solve quadratic programming problems in SVM .

由于支持向量机的改进算法众多，各有优缺点，本文在前人研究基础之上在支持向量机中引入量子粒子群算法来解决二次规划问题。

Therefore , this paper put forward a quantum particle swarm ( QPSO ) algorithm ( overall optimization algorithm , its convergence is faster ) optimize the DV-Hop algorithm for these two obvious flaws for the location of the localization node .

因此，针对这个明显的缺陷，本文提出用量子粒子群（QPSO）算法（全局寻优的算法，其收敛速度较快）优化DVHop算法，定位节点的位置。

This article will try to use the particle swarm optimization ( QPSO ) [ 3 ] which based on the quantum act , the fuzzy clustering algorithm optimization , the genetic algorithms and the quad tree partition method in a combination in fractal image compression .

本文尝试着将基于量子行为粒子群优化算法（QPSO）[3][4]、模糊聚类优化算法，遗传算法[5]与四叉树分割方法相结合应用于分形图像压缩。