Finite-time synchronisation of delayed fractional-order coupled neural networks
Shuailei Zhang, Xinge Liu, Xuemei Li
Abstract
This paper considers the global synchronisation and finite-time synchronisation for a class of delayed fractional-order complex neural networks (DFOCNNs). Based on the properties of fractional-order calculus and the Razumikhin-type Lyapunov theorem of a fractional-order system, two new lemmas are proved. These lemmas are employed to formulate a couple of novel criteria for both finite-time synchronisation and global synchronisation of DFOCNNs. Moreover, the upper bound of the setting time for synchronisation is given. Three examples are provided to verify the effectiveness of the obtained results.
Topics & Concepts
Order (exchange)Artificial neural networkMathematicsClass (philosophy)Control theory (sociology)Type (biology)Synchronization (alternating current)Fractional calculusUpper and lower boundsApplied mathematicsComputer scienceTopology (electrical circuits)Control (management)Mathematical analysisArtificial intelligenceCombinatoricsEconomicsFinanceEcologyBiologyNeural Networks Stability and Synchronizationstochastic dynamics and bifurcationNonlinear Dynamics and Pattern Formation