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Artificial Neural Networks for Modelling

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Artificial Intelligence in Industrial Decision Making, Control and Automation

Part of the book series: Microprocessor-Based and Intelligent Systems Engineering ((ISCA,volume 14))

Abstract

The technique of subsymbolic information processing is based on the functioning of the human brain. It appeals to the idea of black-box modelling, using biologically inspired models of the human brain. When using these models, tools are provided to implement arbitrary complex functions. No explicit knowledge is needed to apply these techniques, in contrast with that which is necessary in the application of symbolic AI techniques based on logic. In symbolic AI systems, the knowledge is represented explicitly, for example by using production rules.

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References

  1. Albus J.S. (1975). A New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC), Transactions of the ASME, pp. 220–227, September.

    Google Scholar 

  2. Albus J.S. (1975). Data Storage in the Cerebellar Model Articulation Controller (CMAC). Transactions of the ASME, pp. 228–233, September.

    Google Scholar 

  3. Billings S.A., H.B. Jamaluddin and S. Chen (1992). Properties of neural networks with applications to modelling nonlinear dynamical systems. International Journal of Control, Vol. 55, No. 1, pp. 193–224.

    Article  MathSciNet  MATH  Google Scholar 

  4. Chen S., S.A. Bilings and P.M. Grant (1990). Nonlinear system identification using neural networks. International Journal of Control, Vol. 51, No. 6, pp. 1191–1214.

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, S. et al. (1991). Orthogonal least squares learning algorithm/or radial basis function networks. IEEE Transactions on Neural Networks, Vol. 2, No. 2, p.p. 302–309.

    Article  Google Scholar 

  6. Chen, S. and S.A. Billings (1992). Neural networks for nonlinear dynamic system modelling and identification. International Journal of Control, Vol. 56, No. 2, p.p. 319–346.

    Article  MathSciNet  MATH  Google Scholar 

  7. Cotter N.E. (1990). The Stone-Weierstrass Theorem and Its Application to Neural Networks. IEEE Transactions on Neural Networks, Vol. 1, No. 4, pp.290–295, December.

    Article  MathSciNet  Google Scholar 

  8. Cowan, C.F.N. and Adams, P.F. Non-linear system modelling: concept and application. Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing. May 1984.

    Google Scholar 

  9. Cox M.G. (1971). Curve fitting with piecewise polynomials. Journal of Inst. Mathematical Applications, Vol. 8, pp.36–52.

    Article  MATH  Google Scholar 

  10. Cox M.G. (1984). Practical spline approximation. In: P.R. Turner (Ed): Topics in Numerical Analysis, Lecture notes in Mathematics 965, New York. Springer Verlag, pp.79–112.

    Google Scholar 

  11. Haber, R. Structural identification of quadratic block-oriented models based on estimated Volterra kernels. International Journal of Systems Science. Vol. 20, No. 8, p.p. 1355–1380, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  12. Hunt, K.J., D. Sbarbaro, R. Zbikowski and P.J. Gawthrop (1992). Neural Networks for Control Systems -A Survey. Automatica, Vol. 28, No. 6, pp. 1083–1112, Pergamon Press Ltd, U.K.

    Article  MathSciNet  MATH  Google Scholar 

  13. Kolmogorov A.N. (1957). On the representation of continuous functions of many variables by superposition of continous functions of one variable and addition. Doklady Akademy Nauk SSSR (N.S.), 114:953–956. Translation in American Mathematical Society Translations, Series 2, 28, pp. 55–59, 1963.

    MathSciNet  MATH  Google Scholar 

  14. LEONTARITIS I.J. AND S.A. BILLINGS (1987). Model selection and validation methods for non-linear systems. Int. Journal of Control, vol. 45, no. 1, pp. 311–341.

    Article  MATH  Google Scholar 

  15. Narendra K.S. and K. Parthasarathy (1990). Identification and Control of Dynamical Systems Using Neural Networks, IEEE Transactions on Neural Networks, Vol.1, No. 1, March, pp.4–27.

    Article  Google Scholar 

  16. Narendra K.S. and K. Parthasaraty (1991). Gradient Methods for the Optimization of Dynamical Systems Containing Neural Networks. IEEE Transactions on Neural Networks, Vol.2, No. 2, pp. 252–262, March.

    Article  Google Scholar 

  17. Rodd M.G., H.B. Verbruggen and A.J. Krijgsman (1992). Artificial Intelligence in Real-Time Control Engineering Applications of Artificial Intelligence, Vol: 5, No: 5, pp. 385–399, Pergamon Press.

    Article  Google Scholar 

  18. Rosenblatt F. (1961). Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC.

    Google Scholar 

  19. Schetzen M. (1981). Nonlinear system modeling based on the Wiener theory. Proceeding of the IEEE, Vol. 69, No. 12, pp. 1557–1573.

    Article  Google Scholar 

  20. Tulleken H.J.A.F. (1992). Grey-box Modelling and Identification Topics. PhD Thesis, Delft University of Technology.

    Google Scholar 

  21. Widrow B. and M.E. Hoff (1960), Adaptive Switching Circuits, In Convention Record, Part 4., IRE Wescon Connection Record, New York, pp. 96–104.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Control Laboratory, Delft University of Technology, P.O. Box 5031, 2600 GA, Delft, The Netherlands

    A. J. Krijgsman, H. B. Verbruggen & P. M. Bruijn

Authors
  1. A. J. Krijgsman
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  2. H. B. Verbruggen
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  3. P. M. Bruijn
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece

    Spyros G. Tzafestas

  2. Department of Electrical Engineering, Delft University of Technology, Delft, The Netherlands

    Henk B. Verbruggen

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© 1995 Springer Science+Business Media Dordrecht

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Krijgsman, A.J., Verbruggen, H.B., Bruijn, P.M. (1995). Artificial Neural Networks for Modelling. In: Tzafestas, S.G., Verbruggen, H.B. (eds) Artificial Intelligence in Industrial Decision Making, Control and Automation. Microprocessor-Based and Intelligent Systems Engineering, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0305-3_10

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  • DOI: https://doi.org/10.1007/978-94-011-0305-3_10

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-4134-8

  • Online ISBN: 978-94-011-0305-3

  • eBook Packages: Springer Book Archive

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