中文摘要：

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.

英文摘要：

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.