Dissipative Sliding-Mode Synchronization Control of Uncertain Complex-Valued Inertial Neural Networks: Non-Reduced-Order Strategy

Runan Guo, Shengyuan Xu, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

Abstract

This study investigates the dissipative synchronization of uncertain complex-valued inertial neural networks with external disturbances using sliding mode control (SMC). In the absence of both variable substitution as well as the equivalent transformations of real-and complex-valued systems, this study focuses directly on the original complex-valued inertial system. First, a suitable integral switching surface (SS) function is proposed. Second, by constructing innovative Lyapunov-Krasovskii functionals and applying the Wirtinger-based integral inequality and reciprocally convex approach, a synchronization criterion is derived on the basis of the linear matrix inequality technique to ensure that the sliding mode dynamics are stable and dissipative. Then, an SMC law and an adaptive SMC law are designed, and the accessibility analysis of the predefined SS is provided. Numerical verifications as well as the superiority and practicality analysis of the proposed approach are provided through four examples.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Circuits and Systems I: Regular Papers
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Artificial neural networks
  • complex-valued neural networks
  • inertial term
  • Linear matrix inequalities
  • Matrices
  • sliding mode control
  • Sliding mode control
  • Switches
  • Synchronization
  • Synchronization
  • time-varying delays
  • Uncertainty

ASJC Scopus subject areas

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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