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
相关论文
共 50 条
  • [1] Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction
    Zhao, Junsheng
    Li, Yongmin
    Yu, Xingjiang
    Zhang, Xingfang
    DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2014, 2014
  • [2] Adaptive Levenberg-Marquardt Algorithm Based Echo State Network for Chaotic Time Series Prediction
    Qiao, Junfei
    Wang, Lei
    Yang, Cuili
    Gu, Ke
    IEEE ACCESS, 2018, 6 : 10720 - 10732
  • [3] Financial time series prediction using artificial neural network based on Levenberg-Marquardt algorithm
    Mammadli, Sadig
    9TH INTERNATIONAL CONFERENCE ON THEORY AND APPLICATION OF SOFT COMPUTING, COMPUTING WITH WORDS AND PERCEPTION, ICSCCW 2017, 2017, 120 : 602 - 607
  • [4] Parallel and separable recursive Levenberg-Marquardt training algorithm
    Asirvadam, VS
    McLoone, SF
    Irwin, GW
    NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS, 2002, : 129 - 138
  • [5] Prediction of SLM-NiTi transition temperatures based on improved Levenberg-Marquardt algorithm
    Yu, Zhenglei
    Xu, Zezhou
    Liu, Ruiyao
    Xin, Renlong
    Li, Lunxiang
    Chen, Lixin
    Sha, Pengwei
    Li, Wanqing
    Zhu, Yining
    Guo, Yunting
    Zhao, Jiale
    Zhang, Zhihui
    Ren, Luquan
    JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, 2021, 15 : 3349 - 3356
  • [6] An Adaptive inverse-QR Recursive Levenberg-Marquardt Algorithm
    Nakornphanom, Kodchakorn Na
    Sitjongsataporn, Suchada
    2013 13TH INTERNATIONAL SYMPOSIUM ON COMMUNICATIONS AND INFORMATION TECHNOLOGIES (ISCIT): COMMUNICATION AND INFORMATION TECHNOLOGY FOR NEW LIFE STYLE BEYOND THE CLOUD, 2013, : 535 - 539
  • [7] Parameter identification of Volterra nonlinear system based on Levenberg-Marquardt recursive algorithm
    Chu, Jie
    Li, Junhong
    Zong, Tiancheng
    2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC, 2022, : 5948 - 5952
  • [8] A Parallel Levenberg-Marquardt Algorithm
    Cao, Jun
    Novstrup, Krista A.
    Goyal, Ayush
    Midkiff, Samuel R.
    Caruthers, James M.
    ICS'09: PROCEEDINGS OF THE 2009 ACM SIGARCH INTERNATIONAL CONFERENCE ON SUPERCOMPUTING, 2009, : 450 - 459
  • [9] The atmospheric model of neural networks based on the improved Levenberg-Marquardt algorithm
    Cui, Wenhui
    Qu, Wei
    Jiang, Min
    Yao, Gang
    OPEN ASTRONOMY, 2021, 30 (01) : 24 - 35
  • [10] Prediction of Oilfield Cementing Quality Based on Levenberg-Marquardt BP Algorithm
    Wang Yuxue
    Zhou Shaohua
    HIGH PERFORMANCE STRUCTURES AND MATERIALS ENGINEERING, PTS 1 AND 2, 2011, 217-218 : 1032 - 1035
12345