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Author:

Wang, Lei (Wang, Lei.) | Qiao, Jun-Fei (Qiao, Jun-Fei.) (Scholars:乔俊飞) | Li, Xiao-Li (Li, Xiao-Li.) (Scholars:李晓理)

Indexed by:

EI Scopus PKU CSCD

Abstract:

To avoid singular problem and improve the performance of neural network, a weight initialization method for echo state network(WIESN) is proposed. With Cauchy inequality and linear algebra, the range of optimal initial weights, which is related to input dimension, reservoir dimension, input variables and reservoir state, is determined. The proposed method ensures that the outputs of neurons are in the active region. Simulation results show that the accuracy and training time of the proposed method is better than learning with random initialization. In addition, the time for weight initialization process is negligible comparing with the training process. © 2018, Editorial Office of Control and Decision. All right reserved.

Keyword:

Control engineering Computer simulation Linear algebra

Author Community:

  • [ 1 ] [Wang, Lei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Wang, Lei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 3 ] [Qiao, Jun-Fei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Qiao, Jun-Fei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
  • [ 5 ] [Li, Xiao-Li]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 6 ] [Li, Xiao-Li]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

Reprint Author's Address:

  • [wang, lei]beijing key laboratory of computational intelligence and intelligent system, beijing; 100124, china;;[wang, lei]faculty of information technology, beijing university of technology, beijing; 100124, china

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Source :

Control and Decision

ISSN: 1001-0920

Year: 2018

Issue: 2

Volume: 33

Page: 356-360

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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