Home>Results

  • Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

[会议论文]

Hybrid structure based pso for esn optimization

Share
Edit Delete 报错

Author:

Ahmad, Zohaib (Ahmad, Zohaib.) | Nie, Kaizhe (Nie, Kaizhe.) | Qiao, Junfei (Qiao, Junfei.) (Scholars:乔俊飞) | Unfold

Indexed by:

EI

Abstract:

Recently, the echo state networks (ESNs) have been widely studied. In an ESN, the input weights and internal weights of reservoir are fixed after initialization, only the output weight matrix needs to be optimized. To calculate the output weights of ESN, the particle swarm optimization algorithm (PSO) with hybrid topology is proposed. The structure of the proposed PSO is mixed with regular network with strong exploration ability and scale-free network with good exploration ability. Simulation results show that the proposed ESN has good prediction performance than the traditional ESN. © 2019 IEEE.

Keyword:

Engineering Particle swarm optimization (PSO) Industrial engineering

Author Community:

  • [ 1 ] [Ahmad, Zohaib]Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing; 100124, China
  • [ 2 ] [Nie, Kaizhe]Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing; 100124, China
  • [ 3 ] [Qiao, Junfei]Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing; 100124, China
  • [ 4 ] [Yang, Cuili]Faculty of Information Technology, Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligence System, Beijing; 100124, China

Reprint Author's Address:

Show more details

Related Article:

Source :

Year: 2019

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

30 Days PV: 3

Affiliated Colleges:

Online/Total:106/5907425
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.