On the stability, storage capacity, and design of continuous nonlinear neural networks
IEEE Transactions on Systems, Man and Cybernetics
Multilayer feedforward networks are universal approximators
Neural Networks
Neural network design
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Intelligent optimal control with dynamic neural networks
Neural Networks
Dynamic recurrent neural networks: a dynamical analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive fuzzy control: experiments and comparative analyses
IEEE Transactions on Fuzzy Systems
Experimental studies in nonlinear discrete-time adaptive prediction and control
IEEE Transactions on Fuzzy Systems
Multilayer discrete-time neural-net controller with guaranteed performance
IEEE Transactions on Neural Networks
Multilayer neural-net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Dynamic structure neural networks for stable adaptive control of nonlinear systems
IEEE Transactions on Neural Networks
Adaptive control using neural networks and approximate models
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Runge-Kutta neural network for identification of dynamical systems in high accuracy
IEEE Transactions on Neural Networks
Robust neural-network control of rigid-link electrically driven robots
IEEE Transactions on Neural Networks
Stable neural-network-based adaptive control for sampled-data nonlinear systems
IEEE Transactions on Neural Networks
Nonlinear adaptive trajectory tracking using dynamic neural networks
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Location and stability of the high-gain equilibria of nonlinear neural networks
IEEE Transactions on Neural Networks
A perceptron network for functional identification and control of nonlinear systems
IEEE Transactions on Neural Networks
Absolute stability conditions for discrete-time recurrent neural networks
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
International Journal of Artificial Intelligence and Soft Computing
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This paper presents a new model reference adaptive neuro-control scheme using feedforward neural networks with momentum back-propagation (MBP) learning algorithm. Training is done on-line to tune the parameters of the neuro-controller that provides the control signal. Noting that pre-learning is not required and the structure of overall system is very simple and straightforward. No additional controller or robustifying signal is required. Tracking performance is guaranteed via Lyapunov stability analysis. Both tracking error and neural network weights remain bounded. An interesting fact about the proposed approach is that it does not require a NN being capable of reconstructing globally model non-linearities.