Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach
IEEE Transactions on Fuzzy Systems
Brief Dissipative control for linear discrete-time systems
Automatica (Journal of IFAC)
Robust observer-based output feedback control for fuzzy descriptor systems
Expert Systems with Applications: An International Journal
Global robust exponential stability in lagrange sense for interval delayed neural networks
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
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Takagi-Sugeno (T-S) fuzzy models are often used to represent complex nonlinear systems by means of fuzzy sets and fuzzy reasoning applied to a set of linear sub-models. In this paper, the global robust dissipativity of T-S fuzzy neural networks with interval time-varying delays are investigated. By constructing a proper Lyapunov-Krasovskii functional and using linear matrix inequality (LMI) technique, delay-dependent criteria for checking the global dissipativity and global exponential dissipativity of fuzzy neural networks have been derived in terms of LMI, which can be solved numerically using LMI toolbox in MATLAB. Finally, numerical examples are given to illustrate the effectiveness of the theoretical results.