Description: 在C语言环境下使用的粒子群优化算法，需要给出最大速度、迭代次数、作为中断条件的最小误差等初始条件。-in the C-language environment to the use of the PSO algorithm, the greatest need is speed, the number of iteration, as the smallest disruption error conditions such as initial conditions. Platform: |
Size: 58368 |
Author: |
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Description: 粒子群算法工具箱
该工具箱将PSO算法的核心部分封装起来，提供给用户的为算法的可调参数，用户只需要定义好自己需要优化的函数(计算最小值或者最大值)，并设置好函数自变量的取值范围、每步迭代允许的最大变化量(称为最大速度，Max_V)等，即可自行优化。-Particle Swarm Optimization Toolbox of the Toolkit will be the core of the PSO algorithm package, and made available to the user adjustable parameters for the algorithm, users only need to define their need to optimize the function (calculation of the minimum or maximum), and set good function from the range of variables, each iteration step the maximum allowable variation (known as maximum speed, Max_V) and so on, can self-optimize. Platform: |
Size: 820224 |
Author:张鹤峰 |
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Description: 多阈值图像分类的粒子群优化算法，采用自然方法来提高优化速度和计算量-Multi-threshold image classification of the particle swarm optimization algorithm, using natural methods to improve the optimization speed and computation Platform: |
Size: 2048 |
Author:baiyilieren |
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Description: 粒子群优化算法容易理解，实现简单，优化速度快，收敛性强。常用于解决种类最优化问题。-Particle swarm optimization algorithm easy to understand, easy to achieve, and optimize the speed and strong convergence. Types commonly used in the optimization problem to solve. Platform: |
Size: 1024 |
Author:lxd |
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Description: 这是一个经过改进的PSO算法，是用FORTRAN语言编写的，因为此语言计算速度快，适合PSO的应用，收敛速度明显加快，并且这是改优化后的PSO程序，比标准的速度快。-This is an improved PSO algorithm, are used FORTRAN language, because this language computing speed, suitable PSO applications, significantly speeding up the convergence rate, and change This is the optimized PSO procedures, faster than the standard. Platform: |
Size: 1024 |
Author:xin |
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Description: 微粒群算法与其它进化类算法相类似，也采用“群体”与“进化”的概念，同样也是依据个体（微粒）的适应值大小进行操作。所不同的是，微粒群算法不像其它进化算法那样对于个体使用进化算子，而是将每个个体看作是在n维搜索空间中的一个没有重量和体积的微粒，并在搜索空间中以一定的速度飞行。-Particle swarm optimization algorithm with other similar type of evolution, but also the use of " groups" and " evolution" concept is also based on the individual (particle) size of the operation of fitness. The difference is that unlike other particle swarm optimization evolutionary algorithm as the use of evolution for the individual operator, but as each individual in the n-dimensional search space of a weight and size of the particles, and in the search space the speed of a certain flight. Platform: |
Size: 228352 |
Author:sunguili |
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Description: 基于粒子群算法的高速公路速度可变控制。可以用来仿真-Based on particle swarm optimization variable speed control of the highway. Simulation can be used to Platform: |
Size: 2048 |
Author:罗权 |
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Description: 改进的粒子群算法--自适应粒子群算法，在普通的粒子群算法里面加入了熵和平均粒距的概念，收敛速度大大提高，用C实现-Improved particle swarm- adaptive particle swarm optimization, in which ordinary PSO joined the entropy and the concept of average distance, speed up the convergence, with the C implementation Platform: |
Size: 169984 |
Author:翁海冰 |
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Description: 自适应粒子群算法，自适应粒子群算法，在普通的粒子群算法里面加入了熵和平均粒距的概念，收敛速度大大提高，而且不容易陷入局部最优，能更有效的解决复杂问题。-Adaptive particle swarm algorithm, adaptive particle swarm optimization, in which ordinary PSO joined the entropy and the concept of average distance, speed up the convergence, but not easy to fall into local optimum, more effective solutions to complex problems. Platform: |
Size: 5120 |
Author:陈波 |
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Description: This code expains bayesian particle swarm optimization method.All files have been written on matlab 2007a. This method has been explianed with various benchmark functions. This optimization method can be directly compared with other unconstrained optimization method like GA or pso for effieciency and speed.
Platform: |
Size: 4096 |
Author:missed2010 |
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Description: An Improved PSO Algorithm to Optimize BP Neural Network
Abstract
This paper presents a new BP neural network
algorithm which is based on an improved particle swarm
optimization (PSO) algorithm. The improved PSO (which
is called IPSO) algorithm adopts adaptive inertia weight
and acceleration coefficients to significantly improve the
performance of the original PSO algorithm in global
search and fine-tuning of the solutions. This study uses the
IPSO algorithm to optimize authority value and threshold
value of BP nerve network and IPSO-BP neural network
algorithm model has been established. The results
demonstrate that this model has significant advantages
inspect of fast convergence speed, good generalization
ability and not easy to yield minimal local results Platform: |
Size: 252928 |
Author:dasu |
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Description: 本程序采用粒子群算法进行计算，粒子群具有速度快的特点。-This procedure is calculated using the particle swarm optimization, PSO has the characteristics of speed. Platform: |
Size: 1024 |
Author:里 |
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Description: 本程序采用基本粒子群算法，共有8个测试函数，测试算法对复杂函数的收敛速度和收敛方差-This program uses a Particle Swarm Optimization, a total of eight test functions, test algorithm convergence speed and variance of complex functions Platform: |
Size: 8192 |
Author:liunan |
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Description: 为提高同步电机励磁调节器的响应速度及鲁棒性，提
出一种基于粒子群优化方法的同步电机分数阶鲁棒励磁控
制器的设计方法。-In order to improve the response speed and robustness of the synchronous motor excitation regulator proposed synchronous motor Fractional Robust Excitation controller design method based on particle swarm optimization method. Platform: |
Size: 455680 |
Author:jj |
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Description: 粒子群算法（PSO）是一种基于群体的随机优化技术。与其它基于群体的进化算法相比，它们均初始化为一组随机解，通过迭代搜寻最优解。不同的是：进化计算遵循适者生存原则，而PSO模拟社会。将每个可能产生的解表述为群中的一个微粒，每个微粒都具有自己的位置向量和速度向量，以及一个由目标函数决定的适应度。所有微粒在搜索空间中以一定速度飞行，通过追随当前搜索到的最优值来寻找全局最优值。 -Particle swarm optimization (PSO) is a kind of stochastic optimization technique based on population. Compared with other evolutionary algorithms based on the group, they are initialized to a set of random solutions. The difference is: follow the principle of survival of the fittest evolutionary computation, and PSO simulation of society. Each of the possible solutions is expressed as a particle in the swarm, each particle has its own position vector and velocity vector, and the fitness of a target is determined by the target function. All particles in the search space to a certain speed, by following the current search to find the optimal value to find the global optimal value. Platform: |
Size: 3072 |
Author:Wang |
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Description: Considering the randomness and volatility of wind, a method based on B-spline neural network optimized by particle swarm
optimization is proposed to predict the short-term wind speed Platform: |
Size: 2032640 |
Author:Ephraim Admassu |
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Description: A novel method based on a combination of the Extended Kalman Filter (EKF) with Particle Swarm Optimization (PSO) to estimate the speed and rotor flux of an induction motor driveis presented. The proposed method will be performed in two steps. As a first step, the covariance matrices of state noise and measurement noise will be optimized in an off-line manner by the PSO algorithm. As a second step, the optimal values of the above covariance matrices are injected in our speed-rotor flux estimation loop (on-line).Computer simulations of the speed and rotor-flux estimation have been performed in order to investigate the effectiveness of the proposed method. Simulations and comparison with genetic algorithms (GAs) show that the results are very encouraging and achieve good performances. Platform: |
Size: 665750 |
Author:pudn0507@yahoo.fr |
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Search - speed particle swarm optimization