Abstract: This research study examines global asymptotic stability of Cohen–Grossberg neural networks that involve both discrete time delay terms in neuron states and neutral delay terms in the time derivatives of neurons’ states. In this context, a suitable Lyapunov functional, constructed as a linear combination of three complementary main Lyapunov functional candidates, is employed to present new alternative sufficient criteria guaranteeing global asymptotic stability of neutral-type neural system possessing discrete delay components. The obtained criteria are established through explicit algebraic inequalities that utilize key matrix properties and structural characteristics of the system functions. The proposed results are basically stated in terms of parameters associated with the considered neutral neural system. These conditions are completely independent of the delay terms and can directly be tested by checking a set of simple algebraic inequalities. In order to illustrate some efficiency aspects of the derived stability results, a numerical example is analyzed.

Keywords: Stability Analysis, Neutral Systems, Lyapunov Functionals, Discrete Delay Terms, Cohen-Grossberg Neural Networks, Matrix Theory

Pages: 130-139