Novel robust stability criteria for stochastic hopfield neural networks with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Delay-dependent H∞ and generalized H2 filtering for delayed neural networks
IEEE Transactions on Circuits and Systems Part I: Regular Papers
IEEE Transactions on Neural Networks
Robust state estimation for neural networks with discontinuous activations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal of Computational and Applied Mathematics
Expert Systems with Applications: An International Journal
Synchronization control of a class of memristor-based recurrent neural networks
Information Sciences: an International Journal
State estimation for delayed neural networks
IEEE Transactions on Neural Networks
Robust State Estimation for Uncertain Neural Networks With Time-Varying Delay
IEEE Transactions on Neural Networks
Delay-Slope-Dependent Stability Results of Recurrent Neural Networks
IEEE Transactions on Neural Networks - Part 1
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In this paper, the delay-dependent 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"~ 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.