The H∞ control problem
Stability analysis and design of fuzzy control systems
Fuzzy Sets and Systems
Identification of nonlinear dynamical systems using multilayered neural networks
Automatica (Journal of IFAC)
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Passivity Analysis of Dynamic Neural Networks with Different Time-scales
Neural Processing Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Stable dynamic backpropagation learning in recurrent neural networks
IEEE Transactions on Neural Networks
H∞-learning of layered neural networks
IEEE Transactions on Neural Networks
Stabilization of uncertain fuzzy control systems via a new descriptor system approach
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Passive and exponential filter design for fuzzy neural networks
Information Sciences: an International Journal
Evolving intelligent algorithms for the modelling of brain and eye signals
Applied Soft Computing
Evolving intelligent system for the modelling of nonlinear systems with dead-zone input
Applied Soft Computing
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In this paper, we propose some new results on stability for Takagi-Sugeno fuzzy delayed neural networks with a stable learning method. Based on the Lyapunov-Krasovskii approach, for the first time, a new learning method is presented to not only guarantee the exponential stability of Takagi-Sugeno fuzzy neural networks with time-delay, but also reduce the effect of external disturbance to a prescribed attenuation level. The proposed learning method can be obtained by solving a convex optimization problem which is represented in terms of a set of linear matrix inequalities (LMIs). An illustrative example is given to demonstrate the effectiveness of the proposed learning method.