A Hybrid Forecasting Model Based on Chaotic Mapping and Improved v-Support Vector Machine

被引：0
|
作者：
Wu, Qi [1 ]
Yan, Hongsen [1 ]
Yang, Hongbing [1 ]
机构：
[1] Southeast Univ, Sch Automat, Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing 210096, Peoples R China
来源：
PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE FOR YOUNG COMPUTER SCIENTISTS, VOLS 1-5 | 2008年
关键词：
Chaos theory; support vector machine; embedded; genetic algorithm; demand forecasting;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Aiming at the product demand series with multi-dimension, small samples, nonlinearity and multi-apex in manufacturing enterprise, chaos theory is combined with support vector machine, and a kind of chaotic support vector machine named Cv-SVM is proposed. And then, a product demand forecasting method and its relevant parameter-choosing algorithm are put forward. The results of application in car demand forecasting show that the forecasting method based on Cv-SVM is effective and feasible.
引用
收藏
页码：2701 / 2706
页数：6
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