Finite-Time Synchronization of Neural Networks With Infinite Discrete Time-Varying Delays and Discontinuous Activations

Yin Sheng, Zhigang Zeng, Tingwen Huang

2021IEEE Transactions on Neural Networks and Learning Systems36 citationsDOI

Abstract

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

Topics & Concepts

Settling timeSynchronization (alternating current)Control theory (sociology)Discrete time and continuous timeDifferential inclusionController (irrigation)Bounded functionComputer scienceArtificial neural networkB-theory of timeState (computer science)Differential (mechanical device)MathematicsControl (management)Topology (electrical circuits)AlgorithmMathematical analysisControl engineeringEngineeringArtificial intelligencePhysicsAgronomyStatisticsAerospace engineeringBiologyStep responseCombinatoricsQuantum mechanicsNeural Networks Stability and SynchronizationAdvanced Memory and Neural ComputingDistributed Control Multi-Agent Systems