没有参数的系统辨识方法与模型估计方式  

Method of nonparametric system identification and model estimation

作　　者：刘开第[1] 庞彦军[1] 马丽涛[1] 

机构地区：[1]河北工程大学不确定数学研究所,河北邯郸056038

出　　处：《控制与决策》2011年第10期1459-1462,1468,共5页Control and Decision

基　　金：国家自然科学基金项目(60874116;60940036);河北省自然科学基金项目(F2009000857)

摘　　要：基于模型类/参数模式的传统系统辨识,虽然囊括了系统辨识几乎所有的成果,理论也趋于成熟,但不宜使用在空间中分布不均匀且数量相对少的数据.鉴于此,提出针对这类数据建模的无参数系统辨识研究方向,讨论基于代表点和加权距离的无参数系统辨识方法,给出基于分类一致性准则的模型估计方式.与传统系统辨识的区别是,"没有参数"并且从实质上改变估计模型的方式.用IRIS,Breast Cancer等典型数据检验了模型的有效性.Although traditional system identification methods based on model class/parameter mode include almost all the results of system identification and the theory is also mature,it is not appropriate to the relatively less data which is uneven distributed in space.For modeling such data,nonparametric system identification research field is found.Based on representative points and the weighted distance,this paper discusses nonparametric system identification,and proposes model estimation method based on classification ＂consistency＂ criterion as well.Finally,with typical data such as IRIS and Breast Cancer data,the effectiveness of model is verified.

关 键 词：系统辨识 无参数辨识 指标分类权 分类一致性 学习算法 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]