Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
406899 | Neurocomputing | 2014 | 9 Pages |
•H∞H∞ state estimation of neural networks has been investigated.•Discrete and distributed time varying delays are considered.•Numerical examples and simulation have been given to demonstrate the effectiveness of presented results.
In this paper, the delay-dependent H∞H∞ state estimation of neural networks with a mixed time-varying delay is considered. By constructing a suitable Lyapunov–Krasovskii functional with triple integral terms and using Jensen inequality and linear matrix inequality (LMI) framework, the delay-dependent criteria are presented so that the error system is globally asymptotically stable with H∞H∞ performance. The activation functions are assumed to satisfy sector-like nonlinearities. The estimator gain matrix for delayed neural networks can be achieved by solving LMIs, which can be easily facilitated by using some standard numerical packages. Finally a numerical example with simulation is presented to demonstrate the usefulness and effectiveness of the obtained results.