A parameterised method on global exponential stability of memristive inertial neural networks with delays

In this article, global exponential stability (GES) of a class of memristive inertial neural networks (MINNs) with delays is studied using a parameterised method. That is, by introducing a suitable parameter to represent the state variables and first-order derivatives of neurons, the stability of the system is directly analysed. The obtained stability conditions are simple linear scalar inequalities (LSIs). This method does not require the construction of a Lyapunov–Krasovskii function (LKF) or complex variable transformations, which greatly reduces computational complexity. Moreover, the obtained stability conditions are simpler in form and easier to verify using mathematical software tools, and the considered delays can be extended to the case of proportional delays (PDs). Finally, numerical examples are given to demonstrate the effectiveness of the employed method.