Improved Quasiuniform Stability for Fractional Order Neural Nets with Mixed Delay
Omar Naifar, Assaad Jmal, A. M. Nagy, Abdellatif Ben Makhlouf
Abstract
In the present paper, a quasiuniform stability result for fractional order neural networks with mixed delay is developed, based on the generalized Gronwall inequality and the Caputo fractional derivative. Sufficient conditions are derived to ensure the quasiuniform stability of the considered neural nets system. A clarification example is carried out not only to validate the authors’ theoretical results but also to show the superiority of the developed work (in terms of improved stability), compared with other similar works already published in the literature.
Topics & Concepts
Stability (learning theory)Artificial neural networkFractional calculusOrder (exchange)MathematicsApplied mathematicsStability conditionsControl theory (sociology)Work (physics)Derivative (finance)Computer scienceArtificial intelligenceEngineeringStatisticsMachine learningEconomicsMechanical engineeringFinancial economicsDiscrete time and continuous timeControl (management)FinanceNeural Networks Stability and SynchronizationNeural Networks and ApplicationsFractional Differential Equations Solutions