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Volumn 18, Issue 2-3, 1997, Pages 149-166

Neural networks in process control: Model-based and reinforcement trained controllers

Author keywords

Adaptive control; Artificial intelligence; Genetic algorithms; Identifiers; Model based control; Neural networks; Nonlinear systems; Predictive control; Reinforcement training; Software tools

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; AGRICULTURE; BACKPROPAGATION; BIOREACTORS; COMPUTER SIMULATION; COMPUTER SOFTWARE; GENETIC ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); LEARNING SYSTEMS; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; PREDICTIVE CONTROL SYSTEMS;

EID: 0031208734     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0168-1699(97)00027-6     Document Type: Article
Times cited : (6)

References (16)
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    • Challenging control problems
    • Miller, T., Sutton, R.S., Werbos, P.J. (Eds.). MIT Press, Cambridge, MA
    • Anderson, C.W., Miller, W.T., 1990. Challenging control problems. In: Miller, T., Sutton, R.S., Werbos, P.J. (Eds.), Neural Networks for Control. MIT Press, Cambridge, MA.
    • (1990) Neural Networks for Control
    • Anderson, C.W.1    Miller, W.T.2
  • 4
    • 0037700420 scopus 로고
    • Reinforcement learning and recruitment mechanism for adaptive distributed control
    • preprint, Delft, The Netherlands. Pergamon Press, Oxford
    • Bersini, H., 1992. Reinforcement learning and recruitment mechanism for adaptive distributed control. In: Proceedings of the 1992 Symposium on Artificial Intelligence in Real-Time Control, preprint, Delft, The Netherlands. Pergamon Press, Oxford, pp. 331-337.
    • (1992) Proceedings of the 1992 Symposium on Artificial Intelligence in Real-time Control , pp. 331-337
    • Bersini, H.1
  • 5
    • 0024861871 scopus 로고
    • Approximations by superpositions of a sigmoidal function
    • Cybenko, G., 1989. Approximations by superpositions of a sigmoidal function. Math. Control Signals Systems 2, 303-314.
    • (1989) Math. Control Signals Systems , vol.2 , pp. 303-314
    • Cybenko, G.1
  • 7
    • 0026838119 scopus 로고
    • Iterative inversion of neural networks and its application to adaptive control
    • March 1992
    • Hoskins, D.A., Hwang, J.N., Vagners, J., 1992. Iterative inversion of neural networks and its application to adaptive control. IEEE Trans. Neural Networks 3 (2), March 1992.
    • (1992) IEEE Trans. Neural Networks , vol.3 , Issue.2
    • Hoskins, D.A.1    Hwang, J.N.2    Vagners, J.3
  • 8
  • 9
    • 0041935844 scopus 로고
    • Master degree thesis, Delft University of Technology, The Netherlands
    • Jarmulak, J., 1994. NeuroControl Workbench. Master degree thesis, Delft University of Technology, The Netherlands.
    • (1994) NeuroControl Workbench
    • Jarmulak, J.1
  • 10
    • 0041935837 scopus 로고
    • Associative memories: The CMAC approach
    • Boullart, L., Krijgsman, A., Vingerhoeds, R.A. (Eds.), Pergamon Press, Oxford
    • Krijgsman, A.J., 1992. Associative memories: The CMAC approach. In: Boullart, L., Krijgsman, A., Vingerhoeds, R.A. (Eds.), Applications of Artificial Intelligence in Process Control. Pergamon Press, Oxford.
    • (1992) Applications of Artificial Intelligence in Process Control
    • Krijgsman, A.J.1
  • 12
    • 0025399567 scopus 로고
    • Identification and control of dynamical systems using neural networks
    • Narendra, K.S., Parthasarathy, K., 1990. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks 1 (1), 4-27.
    • (1990) IEEE Trans. Neural Networks , vol.1 , Issue.1 , pp. 4-27
    • Narendra, K.S.1    Parthasarathy, K.2
  • 13
    • 0001008029 scopus 로고
    • An efficient method for finding the minimum of a function of several variables without calculating derivatives
    • Powell, M.J.D., 1964. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput J 7, 155-162.
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    • Powell, M.J.D.1
  • 15


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.