Lagrange exponential stability of quaternion‐valued memristive neural networks with time delays
Ruoyu Wei, Jinde Cao, Chuangxia Huang
Abstract
This paper investigates the Lagrange global exponential stability of the quaternion‐valued memristive neural networks (QVMNNs). Two kinds of activation functions based on different assumptions are considered. Then, based on the Lyapunov function approach, decomposition method, and some inequality skills, two novel sufficient conditions for lagrange stability of QVMNNs are provided corresponding to different types of activation functions. Lastly, simulation examples are provided to demonstrate the correctness of our theoretical results.
Topics & Concepts
MathematicsQuaternionCorrectnessStability (learning theory)Exponential stabilityLyapunov functionArtificial neural networkControl theory (sociology)Applied mathematicsActivation functionComputer scienceAlgorithmArtificial intelligenceControl (management)GeometryMachine learningPhysicsNonlinear systemQuantum mechanicsNeural Networks Stability and SynchronizationAdvanced Memory and Neural Computingstochastic dynamics and bifurcation