Singular Perturbation Methods in Control: Analysis and Design
Singular Perturbation Methods in Control: Analysis and Design
Dynamic system identification via recurrent multilayer perceptrons
Information Sciences—Informatics and Computer Science: An International Journal
Passivity Analysis of Dynamic Neural Networks with Different Time-scales
Neural Processing Letters
Some new results on system identification with dynamic neural networks
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Global exponential stability of competitive neural networks with different time scales
IEEE Transactions on Neural Networks
Identification of Extended Hammerstein Systems Using Dynamic Self-Optimizing Neural Networks
IEEE Transactions on Neural Networks
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This paper presents a novel identification method for nonlinear systems including the aspects of fast and slow phenomenon via dynamic multilayer neural networks (NN) with two-time scales. The Lyapunov function and singularly perturbed techniques are used to develop the stable learning procedures for the hidden layers and output layers of the dynamic neural networks model. The proposed learning algorithm is similar to the well-known propagation rule of the multilayer perceptrons but with the novel correction terms which guarantee bounded tracking errors and bounded weights. The passivity approach is used to prove that the proposed neural identifier is robust and avoids the need for persistently exciting (PE) conditions. The effectiveness of the algorithm is illustrated via simulation of an electric DC motor and an induction motor identification.