东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2011, Vol. 32 ›› Issue (9): 1225-1228.DOI: -

• 论著 • 上一篇    下一篇

一种新型的硫容量智能预报方法

年海威;毛志忠;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-19 修回日期:2013-06-19 发布日期:2013-04-04
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家高技术研究发展计划项目(2007AA041401,2007AA04Z194)

A new intelligent prediction method of sulfur capacity

Nian, Hai-Wei (1); Mao, Zhi-Zhong (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2013-06-19 Revised:2013-06-19 Published:2013-04-04
  • Contact: Nian, H.-W.
  • About author:-
  • Supported by:
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摘要： 针对在传统硫容量计算中,机理模型的一些关键参数很难获得这个问题,提出了一种基于AdaBoost和LS-SVM混合的回归方法,对硫容量进行智能预报.其中LS-SVM具有计算速度快,适合小样本回归等优点,而AdaBoost可以将弱学习机加权再组合成强学习机,在预报准确度上要高于单一的LS-SVM回归方法,而且还可以减少参数选择对最终预报结果的影响.仿真实验表明,该方法有着较高的准确度,满足生产要求.

关键词: LS-SVM, AdaBoost, 硫容量, 智能建模, 预报模型

Abstract: In the traditional calculation of sulfur capacity, some parameters in the mechanism model are difficult to obtain. To solve this problem, a regression method was proposed based on the AdaBoost and LS-SVM approaches. Sulfur capacity can be predicted by this method. In the method, LS-SVM has fast computation and it is suitable for the problems with small sample set. The AdaBoost method can combine the weak learning machines into a strong learning machine and its accuracy is higher than single LS-SVM's. Meanwhile, the method can reduce the effect of parameters on final prediction results. The result of the simulation shows that this method can significantly improve the prediction accuracy and meet the production requirement.

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