H-infinity state estimation of stochastic memristor-based neural networks with 
time-varying delays

Bao, H.; Cao, J.; Kurths, Jürgen; Alsaedi, A.; Ahmad, B.

doi:10.1016/j.neunet.2017.12.014

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H-infinity state estimation of stochastic memristor-based neural networks with time-varying delays

Authors

Bao, H.
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Cao, J.
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/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

Alsaedi, A.
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Ahmad, B.
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引用

Bao, H., Cao, J., Kurths, J., Alsaedi, A., & Ahmad, B. (2018). H-infinity state estimation of stochastic memristor-based neural networks with time-varying delays. Neutral Networks, 99, 79-91. doi:10.1016/j.neunet.2017.12.014.

引用: https://publications.pik-potsdam.de/pubman/item/item_22417

要旨

This paper addresses the problem of state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results.