Automatica (Journal of IFAC)
Novel delay-dependent asymptotic stability criteria for neural networks with time-varying delays
Journal of Computational and Applied Mathematics
Technical communique: Improved stability criteria and controller design for linear neutral systems
Automatica (Journal of IFAC)
Journal of Computational and Applied Mathematics
Convergence for a class of delayed recurrent neural networks without M-matrix condition
Journal of Computational and Applied Mathematics
Technical communique: New conditions for delay-derivative-dependent stability
Automatica (Journal of IFAC)
Novel robust stability criteria for stochastic hopfield neural networks with time delays
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Circuits and Systems Part I: Regular Papers
New Lyapunov-Krasovskii functionals for global asymptotic stability of delayed neural networks
IEEE Transactions on Neural Networks
Exponential stability on stochastic neural networks with discrete interval and distributed delays
IEEE Transactions on Neural Networks
Automatica (Journal of IFAC)
Stability Analysis for Neural Networks With Time-Varying Interval Delay
IEEE Transactions on Neural Networks
Hi-index | 7.29 |
In this paper, the asymptotical stability is investigated for a class of delayed neural networks (DNNs), in which one improved delay-partitioning idea is employed. By choosing an augmented Lyapunov-Krasovskii functional and utilizing general convex combination method, two novel conditions are obtained in terms of linear matrix inequalities (LMIs) and the conservatism can be greatly reduced by thinning the partitioning of delay intervals. Moreover, the LMI-based criteria heavily depend on both the upper and lower bounds on time-delay and its derivative, which is different from the existent ones. Though the results are not presented via standard LMIs, they still can be easily checked by resorting to Matlab LMI Toolbox. Finally, three numerical examples are given to demonstrate that our results can be less conservative than the present ones.