基于递归约简的在线自适应最小二乘支持向量回归机  

Online adaptive least squares support vector regression based on recursion and reduction

作　　者：刘毅男 张胜修 张超 

机构地区：[1]第二炮兵工程大学自动控制工程系,西安710025

出　　处：《控制与决策》2014年第1期50-56,共7页Control and Decision

基　　金：国家863计划项目(2011AA7053016)

摘　　要：鉴于传统在线最小二乘支持向量机在解决时变对象的回归问题时,模型跟踪精度不高,支持向量不够稀疏,结合迭代策略和约简技术,提出一种在线自适应迭代约简最小二乘支持向量机.该方法考虑新增样本与历史数据共同作用对现有模型产生的约束影响,寻求对目标函数贡献最大的样本作为新增支持向量,实现了支持向量稀疏化,提高了在线预测精度与速度.仿真对比分析表明该方法可行有效,较传统方法回归精度高且所需支持向量数目最少.The tracking accuracy of the traditional online least squares support vector regression in solving regression problem of the time-varying objects is not high enough and support vectors are not sparse. To deal with this problem, an online adaptive recursive reduced least squares support vector regression is proposed by combining with the iterative strategy and reduced technique. The method considers the constrainable impact on the existing model, which is caused by the joint action of new samples and historical data. Meantime, the training sample leading to the largest reduction in the target function is chosen as the best new support vectors. Then the regression model is simplified, and the prediction time is shortened. Finally, simulation analysis illustrates the effectiveness and feasibility of the presented method. Compared with the traditional algorithms, the method is more accurate and sparse.

关 键 词：最小二乘支持向量回归机 在线 自适应 迭代策略 约简技术 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]