Passivity and absolute stabilization of a class of discrete-time nonlinear systems
Automatica (Journal of IFAC)
Passivity approach to fuzzy control systems
Automatica (Journal of IFAC)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Passivity and Passification of Fuzzy Systems with Time Delays
Computers & Mathematics with Applications
Robust asymptotic stability of uncertain fuzzy BAM neural networks with time-varying delays
Fuzzy Sets and Systems
Neural Processing Letters
Passivity analysis of neural networks with time-varying delays
IEEE Transactions on Circuits and Systems II: Express Briefs
Robust passivity and passification of stochastic fuzzy time-delay systems
Information Sciences: an International Journal
New results for robust stability of dynamical neural networks with discrete time delays
Expert Systems with Applications: An International Journal
New results on passivity analysis of uncertain neural networks with time-varying delays
International Journal of Computer Mathematics
Improved asymptotic stability criteria for neural networks with interval time-varying delay
Expert Systems with Applications: An International Journal
Passivity analysis and passification for uncertain signalprocessing systems
IEEE Transactions on Signal Processing
Paper: Stability results for nonlinear feedback systems
Automatica (Journal of IFAC)
Hi-index | 12.05 |
In this paper, the problem of passivity analysis is investigated for uncertain stochastic fuzzy interval neural networks with time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. For the neural networks under study, a generalized activation function is considered, where the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, new delay-dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs), which can be solved by some standard numerical packages. Finally, numerical examples are given to show the effectiveness and merits of the proposed method.