Fixed-time synchronization of nonlinear coupled memristive neural networks with time delays via sliding-mode control
Xingting Geng, Jianwen Feng, Yi Zhao, Na Li, Jingyi Wang
Abstract
<abstract><p>This article focuses on achieving fixed-time synchronization (FxTS) of nonlinear coupled memristive neural networks (NCMMN) with time delays. We propose a novel integrable sliding-mode manifold (SMM) and develop two control strategies (chattering or non-chattering) to achieve FxTS. By selecting appropriate parameters, some criteria are established to force the dynamics of NCMMN to reach the designed SMM within a fixed time and remain on it thereafter. Additionally, they provide estimations for the settling time (TST). the validity of our results is demonstrated through several numerical examples.</p></abstract>
Topics & Concepts
Settling timeControl theory (sociology)Synchronization (alternating current)Nonlinear systemArtificial neural networkSliding mode controlComputer scienceManifold (fluid mechanics)Mode (computer interface)Control (management)Topology (electrical circuits)MathematicsControl engineeringPhysicsEngineeringArtificial intelligenceOperating systemMechanical engineeringStep responseQuantum mechanicsCombinatoricsNeural Networks Stability and SynchronizationAdvanced Memory and Neural Computingstochastic dynamics and bifurcation