一种在线稀疏LSSVM及其在系统建模中的应用  

An Online Sparse LSSVM and Its Application in System Modeling

作　　者：周欣然[1,2] 滕召胜[1] 

机构地区：[1]湖南大学电气与信息工程学院,湖南长沙410082 [2]中南大学信息科学与工程学院,湖南长沙410075

出　　处：《湖南大学学报（自然科学版）》2010年第4期37-41,共5页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(60872128);国家技术创新基金资助项目(07C26214301740)

摘　　要：为了减少在线最小二乘支持向量机(LSSVM)的计算量和存储空间,提出了一种在线稀疏LSSVM.这种LSSVM利用滑动时间窗中部分时刻的样本作为训练样本集.新时刻的样本总是加入训练样本集;每次删除样本时,若滑动时间窗最前端时刻的样本在训练样本集中,则删除它,否则从训练样本集中选择留一法预测误差最小的样本删除.与现有的在线LSSVM相比,这种在线稀疏LSSVM能用较少的样本学习系统较多的特性,能提高时空效率;与现有的在线稀疏LSSVM相比,它能摆脱陈旧样本的影响,更加适应系统的时变性.系统建模仿真实验表明,该在线稀疏LSSVM能节省时间和空间,具有较高的预测精度.To reduce the computation time and the storage space of online least squares support vector machine （LSSVM）, an online sparse LSSVM was proposed. This LSSVM only takes samples at partial moments among sliding time window as training samples set （TSS）. The new sample is learned necessari- ly. When sample elimination is performed, if the sample at the oldest moment among sliding time window exists in TSS, it will be removed during decremental learning. Otherwise, the sample with the smallest leave-one-out predicting error among TSS is selected and deleted. Compared with the existing online LSSVM, the proposed online sparse LSSVM can learn more characteristic of the system with fewer samples, and heighten time-space efficiency. Compared with the existing online spare LSSVM, it can get rid of the obsolete sample, and better adapt to time-variant properties of system. Numerical simulation results for system modeling have shown that the proposed online sparse LSSVM can save time and space, and provide accurate predictions.

关 键 词：最小二乘支持向量机 学习算法 稀疏性 选择性删除 系统建模 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP273[自动化与计算机技术—控制科学与工程]