PSO-SVR在果酒生物活性物质预测中的应用  

Prediction Model of Bioactive Substances From Wine Based on PSO-SVR

作　　者：陈国超[1] 成新文[1] 

机构地区：[1]四川理工学院计算机学院,四川自贡643000

出　　处：《四川理工学院学报（自然科学版）》2013年第6期51-55,共5页Journal of Sichuan University of Science & Engineering(Natural Science Edition)

基　　金：酿酒生物技术及应用四川省重点实验室开放基金项目(NJ2011-09)

摘　　要：针对BP神经网络和遗传算法对果酒生物活性物质预测存在速度慢和精度低的缺点,建立了基于支持向量回归机(SVR)的果酒生物活性物质预测模型。鉴于支持向量机模型的精度和泛化能力很大程度取决于不敏感损失系数ε、惩罚系数C和RBF核函数的宽度系数γ三个参数,模型采用粒子群算法对三个参数同时进行优化,实现了果酒生物活性物质的非线性预测。仿真结果表明:基于PSOSVR算法的果酒生物活性物质预测模型性能优于所比较的BP神经网络模型和支持向量回归机模型,能有效提高果酒生物活性物质的预测精度和稳定性。To solve the predict presence of slow speed and low precision of the bioactive substances from wine by using BP neural network and Genetic Algorithm, a prediction model based on Support Vector Regression （SVR）has been designed for metal corrosion rate. It is well known that the model complexity and generalization performance of this Support Vector Regression model depend on setting of the three parameters （8, C, ~/）. Using the algorithm called Particle Swarm Optimiza- tion （PSO） to optimize the three parameters at the same time, nonlinear predictive of the bioactive substances from wine was achieved. Simulation results show that the proposed model is superior to the other two models for improving the forecast accuracy and stability.

关 键 词：支持向量机 生物活性物质 预测模型 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]