MITTAG-LEFFLER STABILITY ANALYSIS OF TEMPERED FRACTIONAL NEURAL NETWORKS WITH SHORT MEMORY AND VARIABLE-ORDER
Chuan-Yun Gu, Fengxia Zheng, Babak Shiri
Abstract
A class of tempered fractional neural networks is proposed in this paper. Stability conditions for tempered fractional neural networks are provided by using Banach fixed point theorem. Attractivity and Mittag-Leffler stability are given. In order to show the efficiency and convenience of the method used, tempered fractional neural networks with and without delay are discussed, respectively. Furthermore, short memory and variable-order tempered fractional neural networks are proposed under the global conditions. Finally, two numerical examples are used to demonstrate the theoretical results.
Topics & Concepts
Artificial neural networkStability (learning theory)MathematicsVariable (mathematics)Fractional calculusClass (philosophy)Order (exchange)Applied mathematicsPoint (geometry)Control theory (sociology)Computer scienceMathematical analysisArtificial intelligenceMachine learningControl (management)GeometryFinanceEconomicsNeural Networks Stability and SynchronizationFractional Differential Equations SolutionsModel Reduction and Neural Networks