On fractional variable-order neural networks with time-varying external inputs
Amel Hioual, Adel Ouannas
Abstract
This research discuss the existence, uniqueness, asymptotic stability, and global asymptotic synchronization of a class of Caputo variable-order neural networks with time-varying external inputs. Theory of contraction mapping is used to establish a sufficient condition for determining the existence and uniqueness of the equilibrium point. Using the variable fractional Lyapunov approach, we investigate the asymptotic stability of the unique equilibrium. Synchronization of variable-order chaotic networks is also studied using an effective controller. Three numerical examples are provided to show the efficacy of the results obtained.
Topics & Concepts
UniquenessEquilibrium pointMathematicsExponential stabilityControl theory (sociology)Synchronization (alternating current)Variable (mathematics)Artificial neural networkApplied mathematicsLyapunov stabilityStability (learning theory)Class (philosophy)ChaoticController (irrigation)Stability theoryComputer scienceMathematical analysisDifferential equationNonlinear systemTopology (electrical circuits)Control (management)PhysicsMachine learningCombinatoricsBiologyArtificial intelligenceAgronomyQuantum mechanicsNeural Networks Stability and SynchronizationNeural Networks and Applicationsstochastic dynamics and bifurcation