搜索资源列表
MATLAB-Neural-network-cases
- 共有30个MATLAB神经网络的案例(含可运行程序），包括BP、RBF、SVM、SOM、Hopfield、LVQ、Elman、小波等神经网络；还包含PSO(粒子群）、灰色神经网络、模糊网络、概率神经网络、遗传算法优化等内容。-Neural network cases
pso_svm
- 该文件为粒子群算法优化支持向量机模型参数的matlab代码 支持向量机模型为专门用于处理不平衡数据的成本控制型支持向量机模型 用粒子群算法优化模型中的三个主要参数：C1、C2、sigma-The file is in particle swarm optimization parameters of support vector machine model matlab code for support vector machine model designed for use with
30-case-studies
- MATLAB神经网络30个案例分析__读者调用案例的时候，只要把案例中的数据换成自己需要处理的数据，即可实现自己想要的网络。该书共有30个MATLAB神经网络的案例(含可运行程序），包括BP、RBF、SVM、SOM、Hopfield、LVQ、Elman、小波等神经网络；还包含PSO(粒子群）、灰色神经网络、模糊网络、概率神经网络、遗传算法优化等内容。-30 case studies of the MATLAB Neural Network __ readers call the case, as
psoSVM
- 实现pso优化svm的程序。并有实验数据作了说明-Achieve pso to optimize the procedure of the svm. And experimental data gave an explanation
psoLSSVMcgForClass
- 利用PSO优化SVM中的c和g c1:初始为1.5,pso参数局部搜索能力 c2:初始为1.7,pso参数全局搜索能力 maxgen:初始为200,最大进化数量 sizepop:初始为20,种群最大数量-Support Vector Machine Optimized by Particle Swarm Optimization
PSO_GA_SVM
- 利用遗传算法GA和粒子群算法PSO对SVM进行优化-GA genetic algorithm and particle swarm optimization PSO to optimize the SVM
PSO_SVM-With-LSO
- matlab code for pso-svm with lso
pso_svm
- 粒子群算法优化支持向量机参数，可用于分类-pso svm
psosvm
- 将SVM与粒子群算法(PSO)相结合,充分利用SVM在处理小样本回归问题上具有的独特优越性及PSO全局搜索优化等特点-SVM and particle swarm optimization algorithm (PSO) combined with SVM, makes full use of the unique superiority and the PSO global search optimization features in small sample regression prob
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)
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)