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Author:

Wang, Lei (Wang, Lei.) | Yang, Cuili (Yang, Cuili.) | Qiao, Junfei (Qiao, Junfei.) (Scholars:乔俊飞) | Wang, Gongming (Wang, Gongming.)

Indexed by:

CPCI-S

Abstract:

An abnormal solution might occur during the learning process of echo state network if the least singular value of reservoir state matrix is very close zero. To solve this problem, an echo state network based on Levenberg-Marquardt (LM-ESN) algorithm replacing linear regression for output weights is proposed and a new damping term is given. In the proposed method, it is demonstrated that the output weights sequence has quadratical convergence if the norm of error vector provides a local error bound. Simulations show that the new method could deal with abnormal solution problems effectively, also it has better performance and robustness for time series prediction than some existing methods.

Keyword:

Levenberg-Marquardt algorithm time series prediction echo state network

Author Community:

  • [ 1 ] [Wang, Lei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Yang, Cuili]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Qiao, Junfei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Wang, Gongming]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Wang, Lei]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 6 ] [Yang, Cuili]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 7 ] [Qiao, Junfei]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 8 ] [Wang, Gongming]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China

Reprint Author's Address:

  • [Wang, Lei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Wang, Lei]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017)

ISSN: 2161-2927

Year: 2017

Page: 3899-3904

Language: English

Cited Count:

WoS CC Cited Count: 3

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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