Deutsch
 
Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

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

Urheber*innen

Bao,  H.
External Organizations;

Cao,  J.
External Organizations;

/persons/resource/Juergen.Kurths

Kurths,  Jürgen
Potsdam Institute for Climate Impact Research;

Alsaedi,  A.
External Organizations;

Ahmad,  B.
External Organizations;

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PIKpublic verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

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.
https://doi.org/10.1016/j.neunet.2017.12.014


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