Neural networks for control systems: a survey
Automatica (Journal of IFAC)
On characterizations of the input-to-state stability property
Systems & Control Letters
Robust adaptive control
Passivity Analysis of Dynamic Neural Networks with Different Time-scales
Neural Processing Letters
Nonlinear adaptive trajectory tracking using dynamic neural networks
IEEE Transactions on Neural Networks
Some new results on system identification with dynamic neural networks
IEEE Transactions on Neural Networks
Global exponential stability of competitive neural networks with different time scales
IEEE Transactions on Neural Networks
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In this paper, two Neural Network (NN) identifiers are proposed for nonlinear systems identification via dynamic neural networks with different time scales including both fast and slow phenomena. The first NN identifier uses the output signals from the actual system for the system identification. The on-line update laws for dynamic neural networks have been developed using the Lyapunov function and singularly perturbed techniques. In the second NN identifier, all the output signals from nonlinear system are replaced with the state variables of the neuron networks. The on-line identification algorithm with dead-zone function is proposed to improve nonlinear system identification performance. Compared with other dynamic neural network identification methods, the proposed identification methods exhibit improved identification performance. Three examples are given to demonstrate the effectiveness of the theoretical results.