The H∞ control problem
Stability analysis and design of fuzzy control systems
Fuzzy Sets and Systems
Robust adaptive control
Identification of nonlinear dynamical systems using multilayered neural networks
Automatica (Journal of IFAC)
Stability of Adaptive Controllers
Stability of Adaptive Controllers
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
Recurrent neural networks training with stable bounding ellipsoid algorithm
IEEE Transactions on Neural Networks
SOFMLS: online self-organizing fuzzy modified least-squares network
IEEE Transactions on Fuzzy Systems
NLq theory: checking and imposing stability of recurrentneural networks for nonlinear modeling
IEEE Transactions on Signal Processing
Stable dynamic backpropagation learning in recurrent neural networks
IEEE Transactions on Neural Networks
H∞-learning of layered neural networks
IEEE Transactions on Neural Networks
Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network
IEEE Transactions on Neural Networks
High-order neural network structures for identification of dynamical systems
IEEE Transactions on Neural Networks
H∞ stability conditions for fuzzy neural networks
Advances in Fuzzy Systems
Journal of Control Science and Engineering
Passive and exponential filter design for fuzzy neural networks
Information Sciences: an International Journal
Neural Processing Letters
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 a new $${\mathcal H_\infty}$$ weight learning algorithm (HWLA) for nonlinear system identification via Takagi---Sugeno (T---S) fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, for the first time, the HWLA for nonlinear system identification is presented to reduce the effect of disturbance to an $${\mathcal{H}_{\infty }}$$ norm constraint. The HWLA can be obtained by solving a convex optimization problem which is represented in terms of linear matrix inequality (LMI). An illustrative example is given to demonstrate the effectiveness of the proposed identification scheme.