Advertisement

Introduction

  • Igor Grabec
  • Wolfgang Sachse
Part of the Springer Series in Synergetics book series (SSSYN, volume 68)

Abstract

The progress of natural sciences and technology is closely related to quantitative research work. Its methodology is well established and can be described as follows: An object or phenomenon of interest is first quantitatively explored by various measurements and then the experimental results are represented in terms of empirical relations which may subsequently be analytically described in terms of physical laws. The research work in various fields of natural sciences and technology is performed by scientists, utilizing similar experimental techniques and information processing methods. Therefore, a question arises whether it might be possible to develop a general type of machine that could perform such work autonomously, analogous to robots that do mechanical work on industrial production lines. In order to be able to design such a machine, the general procedures of quantitative research work must first be formalized so that such work can be described as a process similar to that which might be used as an industrial test procedure. And then, this process must be implemented using an appropriate technique.

Keywords

Information Processing System Natural Phenomenon Empirical Information Intelligent Controller Quantitative Science 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    The Handbook of Artificial Intelligence, ed. by A. Barr, P. R. Cohen, and E. A. Feigenbaum (Addison-Wesley Publ. Co., Reading, MA 1989)Google Scholar
  2. 2.
    Self-Organization and Life: From Simple Rules to Global Complexity, Proc. Int. Conf. ECAL’93 (Center for Non-Linear Phenomena and Complex Systems, CP 231, Université Libre de Bruxelles, Brussels 1993)Google Scholar
  3. 3.
    Evolution, Games and Learning, Models for Adaptation in Machines and Nature, Proc. 5th Annual Int. Conf. (Center for Nonlin. Stud., Los Alamos, NM 1985); ed. by D. Farmer, A. Lapedes, N. Packard, B. Wendroff (North-Holland, Amsterdam), Physica, 22D, (1986)zbMATHGoogle Scholar
  4. 4.
    I. Grabec: “Self-organization of neurons described by the maximum entropy principle”, Biol. Cyber., 63, 403–409 (1990)zbMATHCrossRefGoogle Scholar
  5. 5.
    I. Grabec and W. Sachse: “Automatic Modeling of Physical Phenomena: Application to Ultrasonic Data”, J. Appl. Phys., 69 (9), 6233–6244 (1991)ADSCrossRefGoogle Scholar
  6. 6.
    H. Haken: Synergetic Computers and Cognition, A Top-Down Approach to Neural Nets ( Springer, Berlin 1991 )zbMATHGoogle Scholar
  7. 7.
    S. A. Kauffman: The Origins of Order, Self-Organization and Selection in Evolution (Oxford University Press, New York 1993 )Google Scholar
  8. 8.
    T. Kohonen: Self-Organization and Associative Memory ( Springer, Berlin 1989 )Google Scholar
  9. 9.
    C. Mead: A Silicon Model of Early Visual Processing, Neural Networks, 1, 91–97 (1988)CrossRefGoogle Scholar
  10. 10.
    D. E. Rumelhart, J. L. McClelland, and PDP Research Group: Parallel Distributed Processing, Explorations in Microstructure of Cognition ( MIT Press, Cambridge, MA 1988 )Google Scholar
  11. 11.
    Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, ed. by A. White, D. A. Sofge (Van Nostrand Reinhold, New York 1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Igor Grabec
    • 1
  • Wolfgang Sachse
    • 2
  1. 1.Faculty of Mechanical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Theoretical and Applied MechanicsCornell UniversityIthacaUSA

Personalised recommendations