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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Extended dissipative state estimation for memristive neural networks with time-varying delay
Jianying Xiao1, Yongtao Li2, Shouming Zhong3
1School of Sciences, Southwest Petroleum University, Chengdu 610050, PR China; School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
Abstract:
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 l2-l∞ state estimation, H∞ 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.
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