Mean-square asymptotic stability of stochastic inertial neural networks with time-delay and Markovian jump parameters
Summary: This paper investigates the stability of inertial neural networks (INNs) which incorporates the effects of both intrinsic and extrinsic noises along with time-delay. These intrinsic and extrinsic noises are taken to be in the form of Markovian jump parameters and Brownian motion respectively. Required sufficient stability conditions are established in the form of linear matrix inequalities from the construction of Lyapunov-Krasovskii functional. Derived conditions will be delay-dependent which includes information about the bounds of the time-delay and also its derivatives. Theory of Lyapunov stability, Ito calculus and linear matrix inequality are used to derive the main results. Numerical example is given to demonstrate the validity of the derived theoretical results.
scientific article; zbMATH DE number 7488821