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Neural networks in mechatronics


An elementary introduction to the theory of artificial neural networks is given. Principles of their structural composition are presented. Methods for the neural-network training commonly used for different levels of intellectual control of mechatronic systems are formulated and substantiated. Neural-network approaches to typical problems of classification, digital signal processing, data compression, function interpolation and extrapolation, associative behavior, and optimization are stated.

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  1. 1.

    J. A. Anderson, “A simple neural network generating an interactive memory,” Math. Biosci., 14, 197–220 (1972).

    MATH  Article  Google Scholar 

  2. 2.

    A. Galushkin, Neural Computers [in Russian], Vol. 3, ed. A. Galushkin, IPRGR, Moscow (2002).

    Google Scholar 

  3. 3.

    A. Gorban and D. Rossiev, Neural Networks on a PC [in Russian], Nauka, Novosibirsk (1996).

    Google Scholar 

  4. 4.

    S. Grossberg, “Adaptive pattern classification and universal recoding. I. Parallel development and coding of neural feature detectors,” Biol. Cybernet., 23, 121–134 (1976).

    Article  MathSciNet  MATH  Google Scholar 

  5. 5.

    Yu. Guljaev and A. Galushkin, eds., Neural Networks in Signal Processing [in Russian], Radiotekhnika, Moscow (2003).

    Google Scholar 

  6. 6.

    M. T. Hagan, H. B. Demuth, and M. H. Beal, Neural Network Design, PWS Publishing Company, Boston (1995).

    Google Scholar 

  7. 7.

    D. O. Hebb, The Organization of Behavior: A Neuropsychological Theory, Wiley, New York (1949).

    Google Scholar 

  8. 8.

    J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities,” Proc. Natl. Acad. Sci. U.S.A., 79, 2554–2558 (1982).

    Article  MathSciNet  Google Scholar 

  9. 9.

    T. Kohonen, “Correlation matrix memories,” IEEE Trans. Comput., 21, 353–359 (1972).

    MATH  Article  Google Scholar 

  10. 10.

    T. Kohonen, Self-Organization and Associative Memory, Springer, Berlin (1987).

    Google Scholar 

  11. 11.

    W. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bull. Math. Biophysics, 5, 115–133 (1943).

    MATH  Article  MathSciNet  Google Scholar 

  12. 12.

    M. Minsky and S. Papert, Perceptrons, MIT Press, Cambridge (1969).

    MATH  Google Scholar 

  13. 13.

    S. Omatu, M. Khalid, and R. Yusof, Neuro-Control and Its Applications, Springer, London (1996).

    Google Scholar 

  14. 14.

    F. Rosenblatt, “The perceptron: A probabilistic model for information storage and organization in the brain,” Psychological Review, 65, 386–408 (1958).

    Article  MathSciNet  Google Scholar 

  15. 15.

    F. Rosenblatt, Principles of Neurodynamics. Perceptron and the Theory of Brain Mechanisms, Spartan Press, Washington (1961).

    Google Scholar 

  16. 16.

    D. E. Rumelhart and J. L. McClelland, eds., Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1, MIT Press, Cambridge (1986).

    Google Scholar 

  17. 17.

    Ja. Tcypkin and A. Galushkin, eds., Neural Network Theory: History [in Russian], Vol. 5, IPRGR, Moscow (2001).

    Google Scholar 

  18. 18.

    V. Terehoff, D. Efimov, and I. Tukin, Neural Control Systems [in Russian], Vol. 8, ed. A. Galushkin, IPRGR, Moscow (2002).

    Google Scholar 

  19. 19.

    B. Widrow and M. E. Hoff, “Adaptive switching circuits,” in: 1960 IRE WESCON Convention Record, Part 4, IRE, New York (1960), pp. 96–104.

    Google Scholar 

  20. 20.

    B. Widrow and M. A. Lehr, “Thirty years of adaptive neural networks: Perceptron, Madaline, and backpropagation,” Proc. IEEE, 78, 1415–1441 (1990).

    Article  Google Scholar 

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Translated from Fundamentalnaya i Prikladnaya Matematika, Vol. 11, No. 8, pp. 81–103, 2005.

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Golubev, Y.F. Neural networks in mechatronics. J Math Sci 147, 6607–6622 (2007).

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  • Transfer Function
  • Conditioned Stimulus
  • Input Vector
  • Unconditioned Stimulus
  • Learning Rule