重庆大学学报Issue(1)：48-54,7.DOI:10.11835/j.issn.1000-582X.2016.01.007
基于 PSO 优化 LS-SVM 的木材含水率软测量建模
Soft sensor model for wood moisture content based on LS-SVM optimized by PSO
摘要
Abstract
Wood moisture content is an important technical specification in the wood drying process. Considering the strong coupling,large lag non-linear features of the wood drying process and the problem of low precision of wood moisture content detection,we proposed a soft sensor method using least squares support vector machines (LS-SVM)to learn time series data of a non-linear system,and built a soft sensor model of the controlled object.We also used the particle swarm optimization (PSO)algorithm in the moving horizon optimization of the penalty factor and the kernel function parameter of LS-SVM to improve the prediction precision of the soft sensor model.Taking the inner temperature and humidity of a wood drying kiln as the sample data,the wood moisture content at a specific point can be detected with the model based on LS-SVM optimized by PSO,which is denoted by PSO-LSSVM.The simulation reveals that the PSO-LSSVM has a high prediction precision and strong generalization ability,and can fulfill the actual measurement demand of a wood drying control system.关键词
支持向量机/最小二乘法/粒子群优化/软测量/建模Key words
support vector machines/least squares/particle swarm optimization/soft sensor/modeling分类
信息技术与安全科学引用本文复制引用
姜滨,孙丽萍,曹军,季仲致..基于 PSO 优化 LS-SVM 的木材含水率软测量建模[J].重庆大学学报,2016,(1):48-54,7.基金项目
国家林业公益性行业科研专项资助项目（201304502）。Supported by the Forestry Industry Research Special Funds for PublicWelfare Project（201304502）． （）
