Global exponential dissipativity and stabilization of memristor-based recurrent neural networks with time-varying delays

Publication Type:
Journal Article
Citation:
Neural Networks, 2013, 48 pp. 158 - 172
Issue Date:
2013-12-01
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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 22n2-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. © 2013 Elsevier Ltd.
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