Feedforward Nonlinear Control Using Neural Gas Network

Article English OPEN
Machón González, Iván José ; López García, Hilario (2017)
  • Publisher: Hindawi Publishing Corporation
  • Journal: (issn: 1076-2787, eissn: 1099-0526)
  • Related identifiers: doi: 10.1155/2017/3125073
  • Subject: Electronic computers. Computer science | QA75.5-76.95 | Article Subject

Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a... View more
  • References (19)
    19 references, page 1 of 2

    Jakubczyk, B., Sontag, E. D.. Controllability of nonlinear discrete-time systems: a lie-algebraic approach. SIAM Journal on Control and Optimization . 1990; 28 (1): 1-33

    Chen, L., Narendra, K. S.. Identification and control of a nonlinear discrete-time system based on its linearization: a unified framework. IEEE Transactions on Neural Networks . 2004; 15 (3): 663-673

    Narendra, K. S., Parthasarathy, K.. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks . 1990; 1 (1): 4-27

    Narendra, K. S., Lewis, F. L.. Editorial: introduction to the special issue on neural network feedback control. Automatica . 2001; 37 (8): 1147-1148

    Zhang, Y., Wang, J.. Recurrent neural networks for nonlinear output regulation. Automatica. A Journal of IFAC, the International Federation of Automatic Control . 2001; 37 (8): 1161-1173

    Martinetz, T. M., Berkovich, S. G., Schulten, K. J.. ‘Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks . 1993; 4 (4): 558-569

    Hammer, B., Strickert, M., Villmann, T.. Supervised neural gas with general similarity measure. Neural Processing Letters . 2005; 21 (1): 21-44

    Hammer, B., Hasenfuss, A., Schleif, F. M., Villmann, T.. Supervised batch neural gas. Artificial Neural Networks in Pattern Recognition . 2006: 33-45

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