Mean‐square exponential input‐to‐state stability of stochastic delayed recurrent neural networks with local Lipschitz condition
W.-J. Wang
Abstract
We consider the global existence of solutions and input‐to‐state stability for a class of stochastic delayed recurrent neural networks without uniform Lipschitz condition. Under local Lipschitz condition, we find new sufficient conditions that ensure the solutions of given neural networks exist globally and are mean‐square exponentially input‐to‐state stable. Furthermore, we highlight the advantages of our novel results by comparing with some known results as well as a numerical example.
Topics & Concepts
Lipschitz continuityMathematicsExponential stabilityStability (learning theory)Mean squareApplied mathematicsArtificial neural networkState (computer science)Class (philosophy)Stochastic neural networkExponential growthControl theory (sociology)Recurrent neural networkMathematical analysisComputer scienceControl (management)AlgorithmNonlinear systemArtificial intelligenceQuantum mechanicsPhysicsMachine learningNeural Networks Stability and SynchronizationNeural Networks and ApplicationsAdvanced Memory and Neural Computing