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改进PSO-SVM在说话人识别中的应用。通过对粒子群优化算法中惯性权重和全局最优值
的分析，提出了一种根据迭代次数而自适应变化的惯性权重的粒子群优化方法,Improvement in the PSO-SVM speaker recognition applications. Through particle swarm optimization algorithm in the inertia weight and the analysis of the global optimum val
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微粒群算法的课程用ptt,内有学习PSO的课用流程,相信想学习的您,不会错过-Particle Swarm Optimization courses with ptt, there are study classes with PSO flow, I believe you want to study, not to miss
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PSO同遗传算法类似，是一种基于迭代的优化算法-PSO is similar with the genetic algorithm is an iterative optimization algorithm based on
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Particle swarm optimization has been used to solve many optimization problems since it was
proposed by Kennedy and Eberhart in 1995 [4]. After that, they published one book [9] and
several papers on this topic [5][7][13][15], one of which did a s
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This article presents particle swarm optimization (PSO)-based optimal gain tuning of proportional integral (PI)
speed controller in an induction motor (IM) drive (30 hp) with mine hoist load diagram
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：针对粒子群算法进行多极点函数优化时
存在的局部极小点和搜寻效率低的问题，引入了小
生境的思想到粒子群算法中，以粒子的最好位置为
中心，粒子的最好的个体解对应的适应值为半径建
立圆形小生境。stretching
技术，其次对子群体采用解散策略，即当在子群体中找到一个极值点后把子群体解散回归主群体，最
后设置子群体创建时的半径阈值，避免子群体半径过大。该算法解决了标准的NichePS0算法在处理
多峰函数时，极值点的个数依赖于子群体个数及极值点容易出现重复、遗漏
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子群优化算法中,粒子群由多个粒子组成,每个粒子的位置代表优化问题在D维搜索空间中潜在的解。根据各自的位置,每个粒子用一个速度来决定其飞行的方向和距离,然后通过优化函数计算出一个适应度函数值(fitness)。-Subgroup of particle swarm optimization algorithm is composed by a number of particles, each particle' s position represents to optimize the p
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：智能算法如粒子群算法已被应用于PID控制器的参数优化，以弥补传统优化方法容易产生振荡和较大超调量
的不足，但是粒子群算法存在易于早熟的缺点，在分析量子粒子群算法的基础上，提出了使用量子粒子群算法优化PID控
制器的参数。为了兼顾控制系统的各项性能指标，根据控制器的实际要求对各项指标进行加权作为算法的目标函数，对
PID控制器进行多目标寻优。通过2个传递函数实例，分别使用z—N、粒子群算法和量子粒子群算法进行了PID控制器
参数优化设计，并对结果进行了分析。-Abstract：H
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本文提出了一种新的混合模糊动态速度反馈的粒子群的优化(HFDVF-PSO）解决经济调度问题的非光滑的成本考虑阀点效应和多种燃料选择功能。-This paper proposes a new Hybrid Fuzzy Dynamic Velocity Feedback Particle Swarm Optimization (HFDVF-PSO) for solving conomic Dispatch (ED) problem with non-smooth cost functions con
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粒子群优化的kriging近似建模程序包DACE-PSO，matlab程序，比传统的dace程序包优势是：不用确定Sigma参数-Kriging particle swarm optimization modeling package DACE-PSO, matlab program, compared with the traditional dace package advantage is: do not determine the Sigma parameters
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Abstract A particle swarm optimization method with nonlinear
time-varying evolution (PSO-NTVE) is employed in
designing an optimal PID controller for asymptotic stabilization
of a pendubot system. In the PSO-NTVE method,
parameters are determ
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This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are
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This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are
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This work investigates the practical application of
support vector machine (SVM) to power transformer condition
assessment. Partiuclarly, this paper proposes to integrate the
SVM algorithm with two heuristic optimization algorithms which
are
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Shot boundary detection is always an important topic in
digital video processing. It is the first important task of
content-based video retrieval and indexing. In this paper, a
new shot boundary detection algorithm is proposed, based on
Parti
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Particle Swarm Optimization Ihope you can understand the PSO with this PPT -Particle Swarm Optimization Ihope you can understand the PSO with this PPT
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Distributed Generators (DGs) in a microgrid may operate in three different reactive power control strategies, including PV, PQ and voltage droop schemes. This paper proposes a new stochastic programming approach for reactive power scheduling of a mic
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关于PSO算法的最新改进，包括主要的测试函数，以及相关的代码描述-This is an implementation of Particle Swarm Optimization algorithm using
the same syntax as the Genetic Algorithm Toolbox, with some additional
options specific to PSO.
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多峰值函数优化中,基本粒子群算法进化后期收敛速度较慢,提出一种具有可控速度因子的改进粒子群算法.-Multimodal function optimization, PSO late evolutionary convergence is slow, with a controllable speed improvements proposed particle swarm optimization factors.
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粒子群算法与遗传算法的联合的GA-PSO算法运用，带有测试函数-Joint GA-PSO algorithm using particle swarm optimization and genetic algorithm with test function
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