Delay-dependent H∞H∞ state estimation of neural networks with mixed time-varying delays

• 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.