Further results on fixed/preassigned-time projective lag synchronization control of hybrid inertial neural networks with time delays

Guodong Zhang, Jinde Cao, Ardak Kashkynbayev

Research output: Contribution to journalArticlepeer-review

Abstract

This article aims to study fixed-time projective lag synchronization(FXPLS) and preassigned-time projective lag synchronization(PTPLS) of hybrid inertial neural networks(HINNs) with state-switched and discontinuous activation functions(DAFs). By constructing new hybrid fixed-time control and based on theory of non-smooth analysis, we achieve novel results on FXPLS for such HINNs. Through designing novel hybrid preassigned-time control, new criteria on PTPLS of the HINNs is also taken into account. And as distinct from recent works, the FXPLS and PTPLS results are established via non-variable substitution and in a more generalized framework than common synchronization, which also has more extensive practical applications. Finally, example simulations are displayed to set forth the validity of the acquired FXPLS and PTPLS.

Original languageEnglish
Pages (from-to)9950-9973
Number of pages24
JournalJournal of the Franklin Institute
Volume360
Issue number13
DOIs
Publication statusPublished - Sept 2023

Keywords

  • Fixed-time projective lag synchronization
  • Hybrid inertial neural networks
  • Preassigned-time lag projective synchronization
  • Time delays

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications
  • Applied Mathematics

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