Convergence analysis on inertial proportional delayed neural networks
Hong Zhang, Chaofan Qian
Abstract
Abstract This article mainly explores a class of inertial proportional delayed neural networks. Abstaining reduced order strategy, a novel approach involving differential inequality technique and Lyapunov function fashion is presented to open out that all solutions of the considered system with their derivatives are convergent to zero vector, which refines some previously known research. Moreover, an example and its numerical simulations are given to display the exactness of the proposed approach.
Topics & Concepts
Ordinary differential equationInertial frame of referenceConvergence (economics)Artificial neural networkLyapunov functionMathematicsPartial differential equationApplied mathematicsControl theory (sociology)Differential equationComputer scienceMathematical analysisArtificial intelligencePhysicsNonlinear systemControl (management)Quantum mechanicsEconomic growthEconomicsNeural Networks and ApplicationsNeural Networks Stability and SynchronizationFractional Differential Equations Solutions