Global uniform asymptotic stability of memristor-based recurrent neural networks with time delays

Memristor is a newly prototyped nonlinear circuit device. Its value is not unique and changes according to the value of the magnitude and polarity of the voltage applied to it. In this paper, a simplified mathematical model is proposed to characterize the pinched hysteretic feature of the memristor, a memristor-based recurrent neural network model is given, and its global stability is studied. Using differential inclusion, two sufficient conditions for the global uniform asymptotic stability of memristor-based recurrent neural networks are obtained.

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