Title | ||
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l2-l∞ state estimation for discrete-time switched neural networks with time-varying delay. |
Abstract | ||
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This paper is concerned with the l2−l∞ state estimation problem for discrete-time switched neural networks with time-varying delay. The main objective is to design a mode-dependent state estimator such that the error dynamics is exponentially stable with a weighted l2−l∞ performance level. By incorporating the novel l2−l∞ performance analysis approach, the augmented piecewise Lyapunov-like functionals, the discrete Wirtinger-based inequality and the average-dwell-time switching, less conservative sufficient conditions are proposed by means of linear matrix inequalities. A numerical example is given to illustrate the effectiveness and benefits of the obtained results. |
Year | DOI | Venue |
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2018 | 10.1016/j.neucom.2017.12.006 | Neurocomputing |
Keywords | Field | DocType |
State estimation,Discrete-time,Switched neural networks,Time-varying delay,l2−l∞ performance | Applied mathematics,State estimator,Pattern recognition,Matrix (mathematics),Exponential stability,Artificial intelligence,Discrete time and continuous time,Artificial neural network,Mathematics,Piecewise | Journal |
Volume | ISSN | Citations |
282 | 0925-2312 | 1 |
PageRank | References | Authors |
0.34 | 30 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yonggang Chen | 1 | 267 | 20.44 |
Lili Liu | 2 | 2 | 0.69 |
Wei Qian | 3 | 91 | 6.67 |
Yurong Liu | 4 | 3419 | 162.92 |
Fuad E. Alsaadi | 5 | 1818 | 102.89 |