Abstract
A time-delay recurrent neural network (TDRNN) model is presented. TDRNN has a simple structure but far more “depth” and “resolution ratio” in memory. A TDRNN controller for dynamic systems is proposed. A dynamic recurrent back-propagation algorithm is developed and the optimal adaptive learning rates are also proposed to guarantee the global convergence. Numeral experiments for controlling speeds of ultrasonic motors show that the TDRNN has good effectiveness in identification and control for dynamic systems.
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© 2004 Springer-Verlag Berlin Heidelberg
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Xu, X., Lu, Y., Liang, Y. (2004). Time-Delay Recurrent Neural Networks for Dynamic Systems Control. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_16
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DOI: https://doi.org/10.1007/978-3-540-28648-6_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22843-1
Online ISBN: 978-3-540-28648-6
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