New convergence on inertial neural networks with time-varying delays and continuously distributed delays
Qian Cao, Xin Long
Abstract
In this paper, a class of inertial neural networks with bounded time-varying delays and unbounded continuously distributed delays are explored by applying non-reduced order method. Based upon differential inequality techniques and Lyapunov function method, a new sufficient condition is presented to ensure all solutions of the addressed model and their derivatives converge to zero vector, which refines some previously known researches. Moreover, a numerical example is provided to illustrate these analytical conclusions.
Topics & Concepts
Inertial frame of referenceConvergence (economics)Artificial neural networkBounded functionControl theory (sociology)Lyapunov functionComputer scienceFunction (biology)Applied mathematicsMathematicsNonlinear systemMathematical analysisArtificial intelligenceControl (management)PhysicsEvolutionary biologyEconomic growthBiologyQuantum mechanicsEconomicsNeural Networks Stability and SynchronizationAdvanced Mathematical Modeling in EngineeringStability and Controllability of Differential Equations