Quasi-Projective Synchronization of Discrete-Time Fractional-Order Delayed Memristive Neural Networks With Uncertainties
Dandan Li, Hongli Li, Cheng Hu, Haijun Jiang, Jinde Cao
Abstract
This article investigates quasi-projective synchronization (Q-PS) of discrete-time fractional-order delayed memristive neural networks (DFDMNNs) with uncertainties. Firstly, by virtue of some useful inequality skills and basic properties of discrete-time fractional calculus as well as fixed-point theorem, several sufficient criteria on the existence of solutions for DFDMNNs with uncertainties are derived. Furthermore, Q-PS of DFDMNNs is explored under the delayed state feedback controller, and corresponding Q-PS criteria are established. Finally, one numerical example is presented to verify the availability of the theoretical results.
Topics & Concepts
Synchronization (alternating current)Artificial neural networkComputer scienceControl theory (sociology)State (computer science)MathematicsControl (management)Stability (learning theory)MemristorComputer simulationTerm (time)Current (fluid)Neural Networks Stability and Synchronizationstochastic dynamics and bifurcationNeural Networks and Applications