1) particle swarm optimization
微粒群
1.
Double thresholding algorithm for image segmentation based on maximum fuzzy entropy and particle swarm optimization;
基于最大模糊熵和微粒群的双阈值图像分割
2.
A image segmentation algorithm based on particle swarm optimization and 2D fuzzy entropy is presented.
将微粒群算法和二维模糊熵阈值分割法结合,提出了一种基于微粒群和二维模糊熵的图像分割方法。
3.
The particle swarm optimization is applied color quantization of color image in this paper.
将微粒群算法应用于彩色图像的颜色量化。
2) particle swarm
微粒群
1.
A particle swarm optimization algorithm is used to find the best parameter for the neural control system.
针对具有惯性和迟延特性、多输入多输出强耦合的中储式球磨机制粉系统,设计了单神经元解耦控制器,针对神经元控制器参数确定较为困难的问题,采用了微粒群算法对控制器参数进行寻优。
2.
Proposes an improved inertia weight mutation particle swarm optimization to solve the premature convergence problem,and to avoid the slowconvergence in the later convergence phase.
针对微粒群优化算法的早熟收敛和进化后期收敛速度慢等问题,提出了一种改进惯性权重的变异微粒群优化算法。
3.
Its characteristic is that initial particles of particle swarm are generated by orthogonal experimental design, so that these particles can be scattered uniformly over the feasible solution space and the particle swarm of the next generation is generated by means of memory.
基于正交试验设计的最优性以及微粒群中微粒的记忆特征,提出了一种新型的微粒群算法——正交微粒群算法。
3) PSO
微粒群
1.
The Research of Application of Intelligent PID Based on Fuzzy Logic and PSO;
基于模糊逻辑和微粒群算法的智能PID的应用研究
2.
Particle swarm optimization (PSO) is an evolutionary computation technique developed by Dr.
微粒群优化算法(Particle Swarm Optimization, PSO算法)源于鸟群和鱼群群体运动行为的研究,是一种新的群体智能优化算法,是演化计算领域中的一个新的分支。
3.
Comprehensive learning particle swarm optimizer(CLPSO) is studied,which uses a learning strategy whereby all other particles\' historical best information to update a particle\'s velocity,has improved the standard PSO and has avoided premature convergence to some extent.
全面学习微粒群优化算法使用所有其它粒子的历史最好信息来更新粒子速度的策略改进标准微粒群算法,虽然一定程度避免陷入早熟,然而也存在到算法后期收敛速度急剧变慢的问题。
4) particle swarm optimizer
微粒群
1.
A new algorithm for solving nonlinear constrained optimization problems with particle swarm optimizer;
一种非线性约束优化的微粒群新算法
6) Particle swarm optimization(PSO)
微粒群算法
1.
This paper systematically introduces and analyzes the background,the principle,process and parameters of particle swarm optimization(PSO) algorithm,and their influence on optimization performance of PSO.
微粒群算法(PSO)是继蚁群算法提出之后的又一种新的进化计算技术。
2.
The Particle Swarm Optimization(PSO) is an evolutionary computational method,which may be conveniently employed to execute random and global search.
微粒群算法(PSO)是新近出现的一种仿生算法,具有简单容易实现,而且随机搜索的优点,使得搜索不易陷于局部最优。
3.
To resolve this problem,a new GMM optimization method was proposed based on Particle Swarm Optimization(PSO).
为了解决传统高斯混合模型(GMM)对初值敏感,在实际训练中极易得到局部最优参数的问题,提出了一种采用微粒群算法优化GMM参数的新方法。
补充资料：陪群公登箕山赋得群字
【诗文】：
许由去已远，冥莫见幽坟。世薄人不贵，兹山唯白云。
宁知三千岁，复有尧为君。时佐激颓俗，登箕挹清芬。
高节虽旦暮，邈与洪崖群。
【注释】：
【出处】：
全唐诗：卷882-9
许由去已远，冥莫见幽坟。世薄人不贵，兹山唯白云。
宁知三千岁，复有尧为君。时佐激颓俗，登箕挹清芬。
高节虽旦暮，邈与洪崖群。
【注释】：
【出处】：
全唐诗：卷882-9
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条