Skip to main content

Soft Computer-Aided System Theory and Technology (SCAST)

  • Foundations of CAST: Theory and Methodology
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1105))

Abstract

Soft Computer-Aided System Theory and Technology or SCAST is introduced as a branch of CAST whose focus is on computing, system theory, and technology that exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low cost. Soft computing is currently viewed as a junction of fuzzy logic, neural computing, probabilistic reasoning, and genetic algorithms. Soft system theory is based on fuzzy set theory, fuzzy measure theory, rough set theory, and their combinations. Soft technology plays a dual role in SCAST. Its first role is to develop supporting software and hardware for soft computing, while its second role is to develop applications of soft computing and soft systems theory in other areas. These various components of SCAST and their relationship are overviewed.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Black, M., “Vagueness: an exercise in logical analysis.” Philosophy of Science, 4, 1937, pp. 427–455 (reprinted in Intern. J. of General Systems, 17 (2–3), 1990, pp. 107–128).

    Google Scholar 

  2. Choquet, G., “Theory of capacities.” Annales de L'Institut Fourier, 5, 1953–54, pp. 131–295.

    Google Scholar 

  3. Christensen, R., Entropy Minimax Sourcebook, Vol. IV: Applications. Entropy, Lincoln, MA, 1981.

    Google Scholar 

  4. Christensen, R., “Entropy minimax multivariate statistical modeling — I: Theory.” Intern. J. of General Systems, 11(3), 1985, pp. 231–277.

    Google Scholar 

  5. Christensen, R., “Entropy minimax multivariate statistical modeling — II: Applications.” Intern. J. of General Systems, 12(3), 1986, pp. 227–305.

    Google Scholar 

  6. Dubois, D. and H. Prade,Possibility Theory. Plenum Press, New York, 1988.

    Google Scholar 

  7. Dubois, D. and H. Prade, “Rough fuzzy sets and fuzzy rough sets.” Intern. J. of General Systems, 17(2–3), 1990, pp. 191–209.

    Google Scholar 

  8. Dubois, D. and H. Prade, “Putting rough sets and fuzzy sets together.” In: Slowinski, R., ed., Intelligent Decision Support. Kluwer, Boston, 1992, pp. 203–232.

    Google Scholar 

  9. Geer, J. F. and G. J. Klir, “A mathematical analysis of informationprocessing transformation between probabilistic and possibilistic formulations of uncertainty.” Intern. J. of General Systems, 20(2), 1992, pp. 143–176.

    Google Scholar 

  10. Gibbs, J. W., Elementary Principles in Statistical Mechanics. Yale University Press, New Haven (reprinted by Ox Bow Press, Woodbridge, Connecticut in 1981), 1902.

    Google Scholar 

  11. Goldberg, D. E.,Genetic Algorithms. Addison-Wesley, Reading, Mass., 1989.

    Google Scholar 

  12. Guan, J. W. and D. A. Bell,Evidence Theory and Its Applications, Vol. 1. North-Holland, New York, 1991.

    Google Scholar 

  13. Guan, J. W. and D. A. Bell,Evidence Theory and Its Applications, Vol. 2. North-Holland, New York, 1992.

    Google Scholar 

  14. Harmanec, D. and G. J. Klir, “Measuring total uncertainty in Dempster-Shafer theory: A novel approach.” Intern. J. of General Systems, 22(4), 1994, pp. 405–419.

    Google Scholar 

  15. Harmanec, D., G. J. Klir and G. Resconi, “On a modal logic interpretation of Dempster-Shafer theory of evidence.” Intern. J. of Intelligent Systems, (in production), 1994

    Google Scholar 

  16. Hartley, R. V. L., “Transmission of information.” The Bell Systems Technical journal, 7, 1928, pp. 535–563.

    Google Scholar 

  17. Hertz, J., A. Krogh and R. G. Palmer,Introduction to the Theory of Neural Computation. Addison-Wesley, Reading, Mass., 1991.

    Google Scholar 

  18. Holland, J.,Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, Ann Arbor, 1975.

    Google Scholar 

  19. Klir, G. J., “A principle of uncertainty and information invariance.” Int. J. of General Systems, 17(2–3), 1990, pp. 249–275.

    Google Scholar 

  20. Klir, G. J., “Developments in uncertainty-based information.” In: Yovits, M. C., ed., Advances in Computers, Vol. 36. Academic Press, San Diego, 1993, pp. 255–332.

    Google Scholar 

  21. Klir, G. J., “Multivalued logics versus modal logics: Alternative frameworks for uncertainty modelling.” In: Wang, P. P., ed., Advances in Fuzzy Theory and Technology, Vol. 2. Bookwrights Press, Durham, NC, 1994

    Google Scholar 

  22. Klir, G. J. and T. Folger,Fuzzy Sets, Uncertainty, and Information. Prentice-Hall, Englewood Cliffs, NJ, 1988.

    Google Scholar 

  23. Klir, G. J. and D. Harmanec, “On modal logic interpretation of possibility theory.” Intern. J. of Uncertainty, Fuzziness, and Knowledge-Based Systems, 2(2), 1994

    Google Scholar 

  24. Klir, G. J. and B. Yuan,Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, Englewood Cliffs, NJ, 1995.

    Google Scholar 

  25. Kruse, R. and K. D. Meyer,Statistics with Vague Data. D. Reidel, Boston, 1987.

    Google Scholar 

  26. Kuhn, T. S.,The Structure of Scientific Revolutions. Univ. of Chicago Press, Chicago, 1962.

    Google Scholar 

  27. Kyburg, H. E., “Bayesian and non-Bayesian evidential updating.” Artifical Intelligence, 31, 1987, pp. pp.271–293.

    Google Scholar 

  28. Moore, R. E.,Methods and Applications of Interval Analysis. SIAM, Philadelphia, 1979.

    Google Scholar 

  29. Pawlak, Z.,Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Boston, 1991.

    Google Scholar 

  30. Pichler, F. and R. Moreno Diaz, (eds.),Computer Aided Systems Theory — EUROCAST'93. Springer-Verlag, New-York, 1994.

    Google Scholar 

  31. Pichler, F. and H. Schwärtzel, (eds.),CAST Methods in Modeling. Springer-Verlag, New York, 1992.

    Google Scholar 

  32. Resconi, G., G. J. Klir and U. St. Clair,“Hierarchical uncertainty metatheory based upon modal logic.” Intern. J. of General Systems, 21(1), 1992, pp. 23–50.

    Google Scholar 

  33. Resconi, G., G. J. Klir, U. St. Clair and D. Harmanec,“On the integration of uncertainty theories.” Intern. J. of Uncertainty, Fuzziness, and Knowledge-Based Systems, 1(1), 1993, pp. 1–18.

    Google Scholar 

  34. Shafer, G., A Mathematical Theory of Evidence. Princeton Univ. Press, Princeton, N.J, 1976.

    Google Scholar 

  35. Simon, H. A.,The Sciences of the Artificial. M.I.T. Press, Cambridge, Mass., 1969.

    Google Scholar 

  36. Smithson, M.,Ignorance and Uncertainty: Emerging Paradigms. Springer-Verlag, New York, 1989.

    Google Scholar 

  37. Sugeno, M.,Theory of Fuzzy Integrals and its Applications. (Ph. D. dissertation). Tokyo Institute of Technology, Tokyo, 1974.

    Google Scholar 

  38. Sugeno, M.,“Fuzzy measures and fuzzy integrals: A survey.” In: Gupta, M. M., G. N. Saridis and B. R. Gaines, eds., Fuzzy Automata and Decision Processes. North-Holland, Amsterdam and New York, 1977, pp. 89–102.

    Google Scholar 

  39. Walley, P.,Statistical Reasoning With Imprecise Probabilities. Chapman and Hall, London, 1991.

    Google Scholar 

  40. Wang, Z. and G. J. Klir,Fuzzy Measure Theory. Plenum Press, New York, 1992.

    Google Scholar 

  41. Weaver, W., “Science and complexity.” American Scientist, 36, 1948, pp. 536–544.

    Google Scholar 

  42. Yager, R. R., S. Ovchinnikov, R. M. Tong and H. T. Nguyen, eds.,Fuzzy Sets and Applications — Selected Papers by L.A.Zadeh. John Wiley, New York, 1987.

    Google Scholar 

  43. Zadeh, L. A., “Fuzzy sets.” Information and Control, 8(3), 1965, pp. 338–353.

    Google Scholar 

  44. Zadeh, L. A., “Fuzzy sets as a basis for a theory of possibility.” Fuzzy Sets and Systems, 1(1), 1978, pp. 3–28.

    Google Scholar 

  45. Zadeh, L. A., “The birth and evolution of fuzzy logic.” Intern J. of General Systems, 17(2–3), 1990, pp. 95–105.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

George J. Klir Tuncer I. Ören

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Klir, G.J. (1996). Soft Computer-Aided System Theory and Technology (SCAST). In: Klir, G.J., Ören, T.I. (eds) Computer Aided Systems Theory — CAST '94. Lecture Notes in Computer Science, vol 1105. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61478-8_64

Download citation

  • DOI: https://doi.org/10.1007/3-540-61478-8_64

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61478-4

  • Online ISBN: 978-3-540-68600-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics