Extended dissipative state estimation for memristive neural networks with time-varying delay.
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.
School 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. Electronic address: shawion1980@yahoo.com.
Jianying
J
Xiao
College of Chemistry and Chemical Engineering, Southwest Petroleum University, Chengdu 610050, PR China.
Yongtao
Y
Li
School of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu 611731, PR China.
Shouming
S
Zhong
School of Sciences, Southwest Petroleum University, Chengdu 610050, PR China.
Fang
F
Xu
D000465:Algorithms; D003198:Computer Simulation; D008568:Memory; D016571:Neural Networks, Computer; D009474:Neurons; D017711:Nonlinear Dynamics
Extended dissipativity; Memristor; Neural networks; State estimation; Time-varying delay