Global Mittag-Leffler stability for fractional-order quaternion-valued neural networks with piecewise constant arguments and impulses
Yanxi Chen, Qiankun Song, Zhenjiang Zhao, Yurong Liu, Fuad E. Alsaadi
Abstract
This paper is devoted to analysing the global Mittag-Leffler stability of fractional-order quaternion-valued neural networks with piecewise constant arguments and impulses. The quaternion direct method is adopted to address the considered model avoiding any decomposition. By utilising the matrix inequality technique and Lyapunov direct method, some sufficient conditions to guarantee the global Mittag-Leffler stability of equilibrium point for the considered model are obtained in the form of quaternion-valued linear matrix inequalities (LMIs). To certify the validity of the derived result, a numerical example is presented.
Topics & Concepts
QuaternionMathematicsPiecewiseConstant (computer programming)Stability (learning theory)Equilibrium pointApplied mathematicsLinear matrix inequalityMatrix (chemical analysis)Artificial neural networkLyapunov functionOrder (exchange)Exponential stabilityControl theory (sociology)Mathematical analysisNonlinear systemMathematical optimizationComputer scienceDifferential equationControl (management)GeometryArtificial intelligenceComposite materialFinanceProgramming languageMaterials scienceMachine learningQuantum mechanicsEconomicsPhysicsNeural Networks Stability and SynchronizationMatrix Theory and AlgorithmsElasticity and Wave Propagation