Chaos time-series prediction based on an improved recursive Levenberg-Marquardt algorithm

被引：16
|
作者：
Shi, Xiancheng [1 ,2 ]
Feng, Yucheng [1 ,2 ]
Zeng, Jinsong [1 ,2 ]
Chen, Kefu [1 ,2 ]
机构：
[1] South China Univ Technol, Sch Light Ind & Engn, Guangzhou 510641, Guangdong, Peoples R China
[2] State Key Lab Pulp & Paper Engn, Guangzhou 510641, Guangdong, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2017年 / 100卷
关键词：
Recursive algorithm; Levenberg-Marquardt; On-line leaming; Neural networks; NEURAL-NETWORK;
D O I：
10.1016/j.chaos.2017.04.032
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
An improved recursive Levenberg-Marquardt algorithm (RLM) is proposed to more efficiently train neural networks. The error criterion of the RLM algorithm was modified to reduce the impact of the forgetting factor on the convergence of the algorithm. The remedy to apply the matrix inversion lemma in the RLM algorithm was extended from one row to multiple rows to improve the success rate of the convergence; after that, the adjustment strategy was modified based on the extended remedy. Finally, the performance of this algorithm was tested on two chaotic systems. The results show improved convergence. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：57 / 61
页数：5
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