基于数据域描述的模糊支持向量回归  

Fuzzy Support Vector Regression Based on Data Domain Description

作　　者：张英[1] 苏宏业[1] 褚健[1] 

机构地区：[1]浙江大学先进控制研究所工业控制技术国家重点实验室,浙江杭州310027

出　　处：《信息与控制》2005年第1期1-6,共6页Information and Control

基　　金：国家 863计划资助项目(2001AA413020);杰出青年科学基金资助项目(NOYSFC: 60025308)

摘　　要：针对支持向量机中由于噪声和孤立点带来的过拟合问题,提出了一种基于支持向量数据域描述的模糊隶属度函数模型,根据样本到特征空间最小包含超球球心的距离来确定其模糊隶属度.将提出的隶属度模型用于模糊支持向量回归中,二维数据集仿真以及工业PTA氧化过程中 4 CBA浓度预测的实例表明,提出的模型可以有效减小回归误差,提高支持向量机抗噪声的能力.*In order to overcome the overfitting problem caused by noises and outliers in support vector machines (SVM), a fuzzy membership model based on support vector data description (SVDD) is presented in this paper. The membership value to each input sample is confirmed according to its distance to the center of the smallest enclosing hypersphere in the feature space. The proposed model is applied to fuzzy support vector regression (FSVR) for 2 dimensional data set simulation and predicting the concentration of 4 carboxybenzaldhyde (4 CBA) in industrial purified terephthalic acid (PTA) oxidation process. Simulation results indicate that the proposed method actually reduces the error of regression and yields higher accuracy than support vector regression(SVR) does.

关 键 词：支持向量机 数据域描述 模糊隶属度 建模 

分 类 号：TP274[自动化与计算机技术—检测技术与自动化装置]