(1) College of Communication and Control Engineering, Jiangnan University, 1800 Lihu Rd., Wuxi, Jiangsu, 214122, China;(2) CSIRO Division of Mathematical and Information Sciences, Urrbrae, South Australia, 5064, Australia
Abstract:
The issue of state estimation is studied for a class of neural networks with norm-bounded parameter uncertainties and time-varying
delay. Some new linear matrix inequality (LMI) representations of delay-dependent stability criteria are presented for the
existence of the desired estimator for all admissible parametric uncertainties. The proposed method is based on the S-procedure
and an extended integral inequality which can be deduced from the well-known Leibniz–Newton formula and Moon’s inequality.
The results extend some models reported in the literature and improve conservativeness of those in the case that the derivative
of the time-varying delay is assumed to be less than one. Two numerical examples are given to show the effectiveness and superiority
of the results.