title: Robust state estimation for uncertain neural networks with time-varying delay.
abstract: The robust state estimation problem for a class of uncertain neural networks with time-varying delay is studied in this paper. The parameter uncertainties are assumed to be norm bounded. Based on a new bounding technique, a sufficient condition is presented to guarantee the existence of the desired state estimator for the uncertain delayed neural networks. The criterion is dependent on the size of the time-varying delay and on the size of the time derivative of the time-varying delay. It is shown that the design of the robust state estimator for such neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. Finally, two simulation examples are given to demonstrate the effectiveness of the developed approach.
authors.affiliation: Department of Manufacturing Engineeringand Engineering Management, City University of Hong Kong, Kowloon, HongKong, P. R. China. hhuang@student.cityu.edu.hk
authors.forename: He
authors.initials: H
authors.lastname: Huang
authors.forename: Gang
authors.initials: G
authors.lastname: Feng
authors.forename: Jinde
authors.initials: J
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors