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PS0-SVR
- ：针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的 支持向量机软测量模型。考虑到该支持向量回归(SVR)模型的复杂性和冷化特征取决于其三 个参数 ，c， 能否取到最优值，采用粒子群优化(PSO)算法实现对参数 ，c， 的同时寻优。在 此基础上，以饲料用 ．甘露聚糖酶为对象，建立了基于PSO—SVR的发酵过程产物浓度状态预估 模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对 ．甘露聚糖酶 产物浓度的实时在线预估。-In
PSO
- 这是粒子群和支持向量机相互结合的优化算法。里面含有代码注释。-This is a support vector machine and particle swarm optimization algorithm combined with each other. Which contains the code comments.
2013-06-30-09PSO_SVM
- 粒子群优化算法PSO优化最小二乘支持向量机参数的程序，程序简单通用，调试通过的。-Particle Swarm Optimization PSO optimization least squares support vector machine parameters, the program simple and universal, debugging through.
psosvm
- 基于粒子群优化的支持向量机风电功率预测，采用PSO对支持向量机算法进行优化。-Based on PSO support vector machine wind power prediction, using PSO support vector machine algorithm for optimization.
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
PSO_LSSVM
- 粒子群优化最小二乘支持向量机的代码。内附测试数据可直接套用。-PSO least squares support vector machine code. Enclosing the test data can be directly applied.
chapter15
- 基于SVM的数据分类预测—一种最基本遗传算法和粒子群算法对的支持向量机的参数的优化，再此基础上可以对算法进行改进-Data classification based on SVM prediction- one of the most basic genetic algorithm and particle swarm optimization (pso) algorithm, the optimization of the parameters of the support vector ma
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-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)