A General Framework for Stability Analysis of Neutral Cohen–Grossberg Neural Networks with Discrete Delay Terms

This paper studies global asymptotic stability of Cohen–Grossberg neural networks involving discrete time delays in the neuron states and neutral delays in the time derivatives of the neuron states. An appropriate Lyapunov functional, which is defined by the linear combination of three Lyapunov functionals of the quadratic forms, is constructed to determine new criteria for global asymptotic stability of neutral-type neural networks with discrete delay parameters. The proposed stability conditions are established through a set of algebraic inequalities that utilize key matrix properties and parameters of system functions. These criteria are proved to be independent of delay components, and they can be tested by checking some algebraic inequalities. A numerical example is studied to illustrate the efficiency aspects of the derived stability conditions.