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PSO智能计算Java库
- PSO智能计算java库-PSO intelligent computing for java
pso(sphere)
- 标准PSO算法,运用SPHERE测试函数,最终收敛于0-standard PSO algorithm, using SPHERE test function, eventually converge to 0
Demo PSO
- 粒子群优化算法(pso）的源程序。包括基本粒子群算法的程序及其在优化函数方面的应用。-Particle Swarm Optimization (PSO) of the source. Groups including the elementary particles algorithm optimization process and its function in the application.
pso
- matlab的粒子群pso工具箱打开后直接调用函数PSOJ即可(he PSO toolbox of MATLAB opens and calls the function PSOJ directly.)
PSO优化SVM参数
- 粒子群算法优化libsvm参数，可以自行修改，亲测可用(Particle Swarm Optimization (PSO) optimizes the parameters of libsvm, which can be modified by itself and can be used for pro-test.)
01 Economic Dispatching using PSO
- 基于粒子群算法(PSO)的电力系统经济调度，matlab平台。(solve power system economic dispatch problem by PSO algorithm)
PSO-vs-WOA-master
- 该代码用于PSO与WOA的优化性能比较，有绘图，输出等展示(This code is used to compare the optimization performance of PSO and WOA. It is shown in drawing, output and so on.)
pso优化BP
- 使用pso优化神经网络，使风电功率预测达到更高的精度(a pso algorithm used to optmize bp neural network)
GA-PSO
- 本算法为用遗传算法改进粒子群GA-PSO算法，附带含有程序使用说明。(This algorithm uses genetic algorithm to improve particle swarm optimization GA-PSO algorithm, with instructions for the use of the program.)
PSO BP wind power
- 粒子群结合神经网络智能算法优化最值问题。(And the output of the fan is tracked and predicted in real time based on the wind power prediction of the PSO algorithm.)
PSO算法
- 粒子群算法求解标准VRP问题，带有十个小型算例，可运行，适合新手学习。(Particle swarm optimization (PSO) is used to solve the standard VRP Problem, with ten small examples, which can be run, and is suitable for novice learning.)
PSO-SVM
- 利用粒子群优化算法对支持向量机中的核函数参数和惩罚参数进行优化是非常有效的手段，可以大大提高鲁棒性。实际过程中读者可通过下载我上传的代码，简单进行修改和阅读附件论文即可快速掌握相关方面的知识，快速使用这一方法。(Particle swarm optimization (PSO) is a very effective method to optimize the kernel function parameters and penalty parameters of SVM, which can
pso-SVM
- pso优化SVM参数，可运行。MATLAB实现(PSO optimizes SVM parameters, which can be run. MATLAB implementation)
PSO粒子群5种改进算法实例源码
- 用MATLAB编写测试函数的五种PSO改进算法(Five PSO improved algorithms with test functions written in MATLAB)
PSO的PID控制器
- 针对一般的粒子群优化(PSO)学习算法中存在的容易陷入局部最优和搜索精度不高的缺点,对改进型PSO算法进行研究。由于惯性权重系数ω对算法是否会陷入局部最优起到关键的作用,因此,通过改变惯性权重ω的选择,对惯性权重系数采取线性减小的方法,引入改进型的PSO算法。采用改进的PSO算法对PID控制器进行参数优化并把得到的最优参数应用于控制系统中进行仿真。仿真实验结果表明:改进型PSO算法不会陷入局部最优,能得到全局最优的PID控制器的参数,并使得控制系统的性能指标达到最优,控制系统具有较好的鲁棒性。(
PSO提取特征
- PSO提取特征，数据为两个板块，标签和数据分成两个矩阵并分别进行了转置(PSO extracts features. The data is divided into two parts. Label and data are divided into two matrices and transposed respectively.)
PSO-ELM
- PSO-ELM 粒子群算法优化极限学习机(PSO-ELM Particle swarm optimization for extreme learning machine)
改进PSO算法及其测试函数
- 为了更好地改善多目标粒子群优化算法的收敛性和多样性的pso 算法(In order to improve the convergence and diversity of multi-objective particle swarm optimization algorithm, PSO algorithm is proposed)
基于PCA+PSO-ELM的工程费用估计
- 利用主成分分析法结合粒子群（PSO）优化极限学习机(ELM)进行工程费用估计预测(In this paper, principal component analysis (PCA) combined with particle swarm optimization (PSO) optimization extreme learning machine (ELM) is used to estimate and forecast engineering cost)
PSO SVM粒子算法优化的支持向量机
- 使用PSO优化SVR支持向量机模型的代码，有详细的输出及输出，以及代码说明(Use PSO to optimize SVR support vector machine model code, with detailed output and output, as well as code descr iption)