Fractional order uncertain BAM neural networks with mixed time delays: An existence and Quasi-uniform stability analysis
C. Maharajan, C. Sowmiya, Changjin Xu
Abstract
This research investigates the presence of unique solutions and quasi-uniform stability for a class of fractional-order uncertain BAM neural networks utilizing the Banach fixed point concept, the contraction mapping principle, and analysis techniques. In order to guarantee the equilibrium point of fractional-order BAM neural networks with undetermined parameters, some new adequate criteria are devised, and both time delays result in quasi-uniform stability. The acquired results, which are simple to verify in practice, enhance and extend several earlier research works in some ways. Finally, two illustrative examples are provided to show the value of the suggested outcomes.
Topics & Concepts
Contraction principleArtificial neural networkStability (learning theory)Equilibrium pointComputer scienceOrder (exchange)Applied mathematicsFixed-point theoremMathematicsPoint (geometry)Fixed pointControl theory (sociology)Class (philosophy)Mathematical optimizationMathematical analysisArtificial intelligenceMachine learningDifferential equationControl (management)GeometryFinanceEconomicsNeural Networks Stability and SynchronizationNeural Networks and ApplicationsMachine Learning and ELM