Journal
ISA TRANSACTIONS
Volume 64, Issue -, Pages 113-128Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2016.05.007
Keywords
State estimation; Neural networks; Extended dissipativity; Time-varying delay; Memristor
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Funding
- National Natural Science Foundation of China [61202045]
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This paper investigates the problem of extended dissipative state estimation for memristor-based neural networks (MNNs) with time-varying delay. Based on both nonsmooth analysis and the construction of a new Lyapunov-Krasovskii functional, the extended dissipative state estimation criteria are obtained by mainly applying differential inclusions, set-valued maps and many new integral inequalities. The extended dissipative state estimation can be adopted to deal with l(2) - l(infinity) state estimation, H-infinity state estimation, passive state estimation and dissipative state estimation by valuing the corresponding weighting matrices. Finally, two numerical examples are given to show the effectiveness and less conservatism of the proposed criteria. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.
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