Sampled-data Synchronization of Recurrent Neural Networks with Multi-GPUs

Yongsik Jin, Seungyong Han, Jongcheon Park, S. M. Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper investigates the multi-rate sampled-data synchronization problem for recurrent reural networks with the multi-GPUs which have each different variable sampling rate. To handle the multi-GPU system with multi-sampling rate, the sampled-data sychronization error system is expressed as a summation of feedback subsystems with multi-sampling intervals. For the sampled-data controller design, Lyapunov functions with looped functions are constructed to use the information of the multi-rate sampling, and the modified free-matrix inequality is exploited to estimate the tighter upper bound intergral term. Finally, the simulation results show the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2172-2177
Number of pages6
ISBN (Electronic)9781728124858
DOIs
StatePublished - Dec 2019
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Country/TerritoryChina
CityXiamen
Period6/12/199/12/19

Keywords

  • LMIs
  • Recurrent neural networks
  • Sampled-data
  • Synchronization

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