Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays ?
This paper addresses the global exponential dissipativity of memristor-based recurrent neural networks with time-varying delays. By constructing proper Lyapunov functionals and using M M -matrix theory and LaSalle invariant principle, the sets of global exponentially dissipativity are characterized parametrically. It is proven herein that there are 22n2?n 2 2 n 2 ? n equilibria for an n n -neuron memristor-based neural network and they are located in the derived globally attractive sets. It is also shown that memristor-based recurrent neural networks with time-varying delays are stabilizable at the origin of the state space by using a linear state feedback control law with appropriate gains. Finally, two numerical examples are discussed in detail to illustrate the characteristics of the results.
global exponential dissipativity stabilization recurrent neural networks memristor neurodynamics