Synchronization control of a class of memristor-based recurrent neural networks
Information Sciences: an International Journal
Self-Controlled Writing and Erasing in a Memristor Crossbar Memory
IEEE Transactions on Nanotechnology
Information Sciences: an International Journal
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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-matrix theory and LaSalle invariant principle, the sets of global exponentially dissipativity are characterized parametrically. It is proven herein that there are 2^2^n^^^2^-^n equilibria for an 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.