Model-Based Recurrent Neural Network for Fault Diagnosis of Nonlinear Dynamic Systems
Publisher
Physica-Verlag HD
Reference37 articles.
1. Nerrand, O., Roussel-Ragot, P., Urbani, D., Personnaz, L., and Dreyfus, G. (1994), “Training recurrent neural networks: why and how? An illustration in dynamical process modeling,” IEEE Trans. on Neural Networks, vol. 5, no. 2, pp. 178–184. 2. Albus, J. (1975), “A new approach to manipulator control: the cerebellar model articulation controller,” ASME J. of Dynamic System, Measurement and Control, vol. 97, pp. 220–227. 3. Miller, W.T. (1989), “Real time application of neural networks for sensor-based control of robots with vision,” IEEE Trans. on Systems, Man, and Cybernetics, vol. 19, pp. 825–831. 4. Parlos, A.G., Chong, K.T., and Atiya, A.F. (1994), “Application of the recurrent multilayer perceptron in modeling complex process dynamics,” IEEE Trans. on Neural Networks, vol. 5, no. 2, pp. 255266. 5. Srinivasan, A. and Batur, C. (1994), “Hopfield/art-1 neural network-based fault detection and isolation,” IEEE Trans. on Neural Networks, vol. 5, no. 6, pp. 890–899.
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