Mean-square exponential input-to-state stability for stochastic neutral-type quaternion-valued neural networks via Itô’s formula of quaternion version
Runtian Zeng, Qiankun Song
Abstract
The input-to-state stability of stochastic quaternion-valued neural networks with neutral delays is explored in this study. Unlike previous researches, this study treats the neural network as a unified entity, rather than isolating and examining the real and imaginary components separately. Through the construction of a Lyapunov functional and the use of the Itô’s formula of quaternion version, a sufficient criterion for achieving mean-square exponential input-to-state stability is obtained for stochastic quaternion-valued neural networks with neutral delays. Three numerical instances are presented to validate the reliability of the obtained conditions.
Topics & Concepts
QuaternionArtificial neural networkStability (learning theory)MathematicsMean squareExponential stabilityState (computer science)Control theory (sociology)Exponential functionApplied mathematicsReliability (semiconductor)Computer scienceAlgorithmMathematical analysisArtificial intelligenceNonlinear systemMachine learningQuantum mechanicsGeometryPhysicsControl (management)Power (physics)Neural Networks Stability and SynchronizationAdvanced Memory and Neural ComputingControl and Stability of Dynamical Systems