A modified particle swarm optimization for combining forecasting

被引：0
|
作者：
Feng, XY [1 ]
Wan, LM [1 ]
Liang, YC [1 ]
Sun, YF [1 ]
Lee, HP [1 ]
Wang, Y [1 ]
机构：
[1] Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
来源：
PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2004年
关键词：
Particle Swarm Optimization (PSO); combining forecasting; hybrid algorithm;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A modified particle swarm optimization (PSO) algorithm is proposed. Linear constraints in the PSO are added to satisfy normalization conditions for different problems. A hybrid algorithm based on the modified PSO and combining forecasting is presented. Combining forecasting can improve the forecasting accuracy through combining different forecasting methods. The effectiveness of the algorithm is demonstrated through the prediction on the sunspots and the stocks data. Simulated results show that the hybrid algorithm can improve the forecasting accuracy to a great extent.
引用
收藏
页码：2384 / 2389
页数：6
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