Predicting chaotic time series by ensemble self-generating neural networks

被引：0
|
作者：
Inoue, H [1 ]
Narihisa, H [1 ]
机构：
[1] Okayama Univ Sci, Fac Engn, Dept Informat & Comp Engn, Okayama 7000005, Japan
来源：
IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL II | 2000年
关键词：
D O I：
10.1109/IJCNN.2000.857902
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we introduce ensemble self-generating neural networks (ESGNNs) for chaotic time series prediction. ESGNNs are combined the ensemble averaging method with SGNNs. ESGNNs create self-generating neural trees (SGNTs) to shuffle the order of given training data independently, and the network output is averaged of all SGNTs output. We investigate improving capability of ESGNNs for three chaotic time series and compare with the backpropagation neural networks (BPNNs). Experimental results show that using various SGNTs through ensemble averaging method significantly improves the predictive performance of ESGNNs on diverse chaotic time series.
引用
收藏
页码：231 / 236
页数：6
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