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

页码：2701 / 2706

页数：6

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