搜索资源列表
PSO-SampEn--ApproxiEN-RVM
- PSO：粒子群优化算法，用C++和matlab两种语言实现的 RVM:相关向量机，比SVM更高级些 样本熵和近似熵：计算动态系统中时间序列的一个重要手段-PSO: PSO algorithm, using C++ and matlab realization of two languages RVM: relevance vector machine, some more advanced than SVM Sample entropy and approximate ent
PSO
- 使用粒子群算法PSO，优化支持向量机的参数，对数据进行分类。-The use of particle swarm algorithm PSO, optimize the parameters of SVM, to classify the data.
PSO-SVM
- 利用粒子群算法对支持向量机的惩罚因子等进行优化。方法简便有效- Particle swarm optimization for SVM punishment factor optimization. The method is simple and effective
GA-PSO
- 遗传算法和粒子群算法以及网格搜索法优化神经网络SVM的高斯核参数和惩罚参数-Optimization of Genetic Algorithm Neural Network SVM Gaussian kernel parameters and penalty parameter
PSO-SVM
- 粒子群优化算法优化支持向量机（PSO-SVM)-Particle swarm optimization algorithm optimize the SVM
PSO-SVM
- 利用PSO算法优化SVM向量机参数。测试指标为MAPE和均方根误差。-Optimization of SVM Parameters by PSO Algorithm. The test indexes are MAPE and root mean square error.
pso-optimization
- 基于粒子群（pso）的参数寻优，可以做BP和svm的参数寻优。-Based on Particle Swarm (pso) parameter optimization, BP and svm you can do the parameter optimization.
PSO-SVM
- 用粒子群算法PSO,优化支持向量机SVM，提高故障分类精度。-Using particle swarm optimization (PSO, optimization of support vector machine SVM, improve the fault classification accuracy.
PSO--svm
- 用简单粒子群算法PSO优化支持向量机SVM，提高故障分类精度。-Using particle swarm optimization (PSO, optimization of support vector machine SVM, improve the fault classification accuracy.
PSO-LSSVM
- 利用改进PSO算法对LS-SVM进行参数优化，参数 和 的取值范围分别为 和 ，粒子种群数量为 25，迭代次数为 100，惯性权重因子 和 取0.9和0.1，学习因子 和 均取2。-The parameters of PS-SVM are optimized by using the improved PSO algorithm. The range of parameters is 25, the number of particles is 25, the number of iterati
PSO-LSSVM-CLASS
- 经过优化得到的参数组，利用优化的参数构建LS-SVM模型，然后使用训练样本对其进行训练。 利用训练后的LS-SVM对测试样本进行分类，-The optimized parameters are used to construct the LS-SVM model with optimized parameters, and then trained using training samples. Using the trained LS-SVM to classify the tes
PSO-SVM
- 利于PSO优化的SVM，可用于解决软测量建模过程中的非线性问题(SVM, which is beneficial to PSO optimization, can be used to solve the nonlinear problems in soft sensor modeling)
pso-svm one output
- 实现多输入单输出的预测功能，预测精度高，程序操作简单，易于修改应用。(The prediction function of multi input and single output is realized. The prediction accuracy is high, the program operation is simple, and it is easy to modify the application.)
psosvm
- 粒子群优化支持向量机对电池寿命进行预测，利用粒子群优化支持向量机参数(Particle swarm optimization support vector machine is used to predict battery life, and particle swarm optimization is used to support SVM parameters)
parameters selection of svm based on pso
- parameter selection of svm based on pso
PSO-Based-SVR-master
- 该文件为粒子群算法优化支持向量机模型(This document is optimized by Particle Swarm Optimization (SVM) model)
pso-svm电力负荷预测
- 电力负荷预测是电力系统规划的重要组成部分，也是电力系统经济运行的基础，其对电力系统规划和运行都极其重要。利用粒子群算法优化支持向量机更加高效准确预测电力负荷。(The power load forecasting is an important part of the power system planning, and it is also the basis of the economic operation of the power system. It is very important
一些优化算法论文附其程序
- 针对例如SVM等智能算法的参数寻优采用自适应的参数优化(Parameter optimization for intelligent algorithm)
ga_aco_opt_on_anfis_svm-master
- 利用遗传算法、蚁群算法、PSO等对SVM模型进行优化，实现高效分类和回归预测(The SVM model is optimized by genetic algorithm, ant colony algorithm and PSO to achieve efficient classification and regression prediction.)
pso-SVM
- 利用粒子群法寻找出c,g两个参数，从而达到优化支持向量机的目的