Global exponential stability of uncertain memristor-based recurrent neural networks with mixed time delays View Full Text


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Article Info

DATE

2019-04

AUTHORS

Jianmin Wang, Fengqiu Liu, Sitian Qin

ABSTRACT

The global exponential stability of the equilibrium point for uncertain memristor-based recurrent neural networks is studied in this paper. The memristor-based recurrent neural networks considered in this paper are based on a realistic memristor model, and can be considered as the extension of some existing memristor-based recurrent neural networks. By virtue of homomorphic theory, it is proved that the uncertain memristor-based recurrent neural networks have a unique equilibrium point under some mild assumptions. Moreover, the unique equilibrium point is proved to be globally exponentially stable by constructing a suitable Lyapunov functional. Finally, the obtained results are applied to determine the dynamical behaviors and circuit design of the memristor-based recurrent neural networks by some numerical examples. More... »

PAGES

743-755

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13042-017-0759-4

DOI

http://dx.doi.org/10.1007/s13042-017-0759-4

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https://app.dimensions.ai/details/publication/pub.1099700291


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