Multilayer feedforward networks are universal approximators
Neural Networks
Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Linear time-varying systems: control and adaptation
Linear time-varying systems: control and adaptation
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
A genetic algorithm for generating fuzzy classification rules
Fuzzy Sets and Systems
A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems
Automatica (Journal of IFAC)
Tuning of a neuro-fuzzy controller by genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary learning of BMF fuzzy-neural networks using a reduced-form genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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A GA-based adaptive fuzzy-neural controller for a class of multi-input multi-output nonlinear systems, such as robotic systems, is developed for using observers to estimate time derivatives of the system outputs. The weighting parameters of the fuzzy-neural controller are tuned on-line via a genetic algorithm (GA). For the purpose of on-line tuning the weighting parameters of the fuzzy-neural controller, a Lyapunov-based fitness function of the GA is obtained. Besides, stability of the closed-loop system is proven by using strictly-positive-real (SPR) Lyapunov theory. The proposed overall scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system track the desired output trajectories. Finally, simulation results are provided to demonstrate robustness and applicability of the proposed method.