Abstract
This paper is concerned with the problem of passivity analysis for a class of memristive neural networks with mixed time-varying delays and different state-dependent memductance functions. By employing the theories of differential inclusions and set-valued maps, delay-dependent criteria in terms of linear matrix inequalities are obtained for the passivity of the memristive neural networks. Finally, numerical examples are given to illustrate the feasibility of the theoretical results.
Highlights
1 Introduction In, the memristor was predicted by Chua [ ] and considered to be the fourth passive circuit element
3 Main results we present our passivity criteria for system ( . )
5 Concluding remarks In this paper, the problem of circuit design and passivity have been discussed for a class of memristor-based neural networks with mixed time-varying delays and different statedependent memductance functions
Summary
In , the memristor was predicted by Chua [ ] and considered to be the fourth passive circuit element. The first practical memristor device was found by Strukov et al [ ] in. The memristor retains its most recent value when the voltage is turned off, so it re-expresses the retained value when it is turned on the time. Some classes of memristors have nonlinear response characteristics which makes them doubly suitable as artificial neurons. The electronic synapses and neurons was found that they can represent important functionalities of their biological counterparts in [ ]. The simulation of different kinds of memristors has developed rapidly and the studies of memristive neural networks have caused more attention [ – ]. In [ ], Wu and Zeng considered the following memristive neurodynamic system:
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