自然科学版 英文版
自然科学版 英文版
自然科学版 英文版
自然科学版 英文版
英文版编委
自然科学版 英文版
英文版首届青年编委

您目前所在的位置:首页 - 期刊简介 - 详细页面

中南大学学报(自然科学版)

Journal of Central South University

第48卷    第8期    总第276期    2017年8月

[PDF全文下载]    [Flash在线阅读]

    

文章编号:1672-7207(2017)08-2097-08
一种改进的FLS-SVM分类辨识模型及其应用
左红艳1, 2,王涛生2

(1. 中南大学 资源与安全工程学院,湖南 长沙,410083; 2.湖南涉外经济学院 商学院,湖南 长沙,410205)

摘 要: 采用三角形函数隶属度法确定模糊最小二乘支持向量机(fuzzy least squares support vector machine, FLS-SVM)输入参数隶属度,采用自适应变尺度混沌免疫算法优化FLS-SVM的参数,从而构建改进模糊最小二乘支持向量机(improved fuzzy least squares support vector machines, IFLS-SVM)分类辨识模型, 用Ripley数据集、MONK数据集和PIMA数据集进行仿真实验,并用于地下金属矿山采场信号分类辨识与中国国际贸易安全分类辨识。研究结果表明:与LS-SVM分类辨识模型和FLS-SVM分类辨识模型相比,IFLS-SVM分类辨识模型能有效提高带噪声点和异常点数据集的分类精度,且分类辨识精度相对误差较小。

 

关键词: 混沌免疫算法;模糊最小二乘支持向量机;分类辨识

An improved FLS-SVM classification identification model and its application
ZUO Hongyan1, 2, WANG Taosheng2

1. School of Resources and Safety Engineering, Central South University, Changsha 410083, China; 2. School of Business, Hunan International Economics University, Changsha 410205, China

Abstract:A classification and identification model was developed based on improved fuzzy least squares support vector machines(FLS-SVM),in which the fuzzy membership function was set by using triangle function method and its parameters were optimized by an adaptive mutative scale chaos immune algorithm, and an improved fuzzy least squares support vector machines(IFLS-SVM) was constructed. The simulation experiments were conducted on three benchmarking datasets such as Ripley datasets, MONK datasets and PIMA datasets for testing the generalization performance of the classification and identification model, signals from underground metal mines stope wall rock and international trade data in China were diagnosed by the IFLS-SVM classification and identification model. The results show that compared with LS-SVM classification identification model and FLS-SVM classification identification model, the IFLS-SVM classification identification model is valid for improving the analysis accuracy of the data with noises or outliers and IFLS-SVM classification identification model has small relative error.

 

Key words: chaos immune algorithm; fuzzy support vector machines; classification identification

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
版权所有:《中南大学学报(自然科学版、英文版)》编辑部
地 址:湖南省长沙市中南大学 邮编: 410083
电 话: 0731-88879765(中) 88836963(英) 传真: 0731-88877727
电子邮箱:zngdxb@csu.edu.cn 湘ICP备09001153号