Advertisement

General System Identification — Fundamentals and Results

  • B. R. Gaines
Part of the NATO Conference Series book series (NATOCS, volume 5)

Abstract

The term identification was introduced by Zadeh in a 1956 paper [1] as a generic expression for the problem of “determining the input-output relationships of a black box by experimental means.” He cited the various terminologies then prevalent for the same problem: “characterization,” “measurement,” “evaluation,” “gedanken experiments,” etc., and noted that the term “identification” states “the crux of the problem with greater clarity than the more standard terms above.”

Keywords

Inductive Inference Epistemological Problem Gedanken Experiment Grammatical Inference Matical Inference 
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.
    L. A. Zadeh, “On the Identification Problem,” IRE Transactions on Circuit Theory, 3, December 1956, pp. 277–281.Google Scholar
  2. 2.
    P. Eykhoff, System Identification, J. Wiley, London, 1974.Google Scholar
  3. 3.
    B. R. Gaines, “Linear and NonLinear Models of the Human Controller,” International Journal of Man-Machine Studies, 1, pp. 333–360, 1969.CrossRefGoogle Scholar
  4. 4.
    M. Dawkins, and R. Dawkins, “Some Descriptive and Explanatory Stochastic Models of Decision-Making,” Motivational Control Systems Analysis, Academic Press, pp. 119–168, 1974.Google Scholar
  5. 5.
    R. Dawkins, “Hierarchical Organization: A Candidate Principle for Ethology,” in P. P. G. Bateson and R. A. Hinde (ed), Growing Points in Ethology, Cambridge University Press, pp. 7–54, 1976.Google Scholar
  6. 6.
    D. M. Vowles, “Neuroethology, Evolution and Grammar,” in L. R. Aronson et al. (eds), Development and Evolution of Behaviour, W. H. Freeman, pp. 194–215, 1970.Google Scholar
  7. 7.
    K. S. Fu, and T. L. Booth, “Grammatical Inference: Introduction and Survey,” IEEE Trans, Systems, Man and Cybernetics, SMC-5, 1975, pp. 95–111 (Part I), 409–423 (Part II).CrossRefGoogle Scholar
  8. 8.
    K. S. Fu, Syntactic Methods in Pattern Recognition, Academic Press, New York, 1974.Google Scholar
  9. 9.
    B. R. Gaines, “System Identification, Approximation and Complexity,” International Journal of General Systems, 3, pp. 145–174, 1977.CrossRefGoogle Scholar
  10. 10.
    B. R. Gaines, “Behaviour/Structure Transformations Under Uncertainty,” International Journal of Man-Machine Studies, 8, pp. 337–365, 1976.CrossRefGoogle Scholar
  11. 11.
    B. R. Gaines, “Progress in General Systems Research,” Proceedings of First International Conference on Applied General Systems Research: Recent Developments and Trends, Binghamton, N.Y., August 1977.Google Scholar
  12. 12.
    S. Watanabe, “Information-Theoretical Aspects of Inductive and Deductive Inference,” IBM Journal Research and Development, 4, pp. 208–231, 1960.CrossRefGoogle Scholar
  13. 13.
    S. Watanabe, “Symmetry in Physical Laws, Part III, Prediction and Retrodiction,” Reviews of Modern Physics, 27, pp. 179–186, 1955.CrossRefGoogle Scholar
  14. 14.
    R. Solomonoff, “A Formal Theory of Inductive Inference,” Information and Control, 7, pp. 1–22 (Part 1), 224–254 (Part 2), 1964.CrossRefGoogle Scholar
  15. 15.
    R. Solomonoff, “A New Method for Discovering the Grammars for Phrase Structure Languages,” Proc. Int. Conf. Information Processing, UNESCO, Paris, Butterworths, June 1959, pp. 285–290.Google Scholar
  16. 16.
    N. Chomsky, and G. A. Miller, Pattern Conception, Report AFCRC-TN-57–57, August 1957.Google Scholar
  17. 17.
    E. M. Gold, “Language Identification in the Limit,” Information and Control, 10, pp. 447–474, 1967.CrossRefGoogle Scholar
  18. 18.
    J. A. Feldman, “First Thoughts on Grammatical Inference,” A. I. Memo 55, Stanford University, 1967.Google Scholar
  19. 19.
    J. Feldman, “Some Decidability Results on Grammatical Inference and Complexity,” Information and Control, 20, pp. 244–262, 1972.CrossRefGoogle Scholar
  20. 20.
    A. Biermann, and J. Feldman, “A Survey of Results in Grammatical Inference,” in S. Watanabe (ed), Frontiers of Pattern Recognition, Academic Press, New York, pp. 31–53, 1972.Google Scholar
  21. 21.
    J. J. Horning, A Study of Grammatical Inference, Ph.D. Thesis, Stanford University, 1969 (University Microfilms 70–10, 465).Google Scholar
  22. 22.
    A. W. Biermann, and R. Krishnaswamy, “Constructing Programs from Example Computations,” IEEE Trans. Software Engineering, SE-2, pp. 141–153, 1976.CrossRefGoogle Scholar
  23. 23.
    A. R. Patel, Grammatical Inference for Probabilistic Finite State Grammars, Ph.D. Thesis, University of Connecticut, 1972 (University Microfilms 72–32 241).Google Scholar
  24. 24.
    F. J. Maryanski, Inference of Probabilistic Grammars, Ph.D. Thesis, University of Connecticut, 1974 (University Microfilms 75–10 645).Google Scholar
  25. 25.
    J. A. Goguen, “Realization is Universal,” Mathematical Systems Theory, 6, pp. 359–374, 1973.CrossRefGoogle Scholar
  26. 26.
    M. A. Arbib, and E. G. Manes, “Foundations of Systems Theory: Decomposable Systems,” Automatica, 10, pp. 285–302, 1974.CrossRefGoogle Scholar
  27. 27.
    H. Ehrig, Universal Theory of Automata, B. G. Teubner, Stuttgart, 1974.CrossRefGoogle Scholar
  28. 28.
    M. A. Arbib, “Automata and Control Theory—A Rapprochement,” Automatica, 3, pp. 161–189, 1966.CrossRefGoogle Scholar
  29. 29.
    T. L. Fine, Theories of Probability, Academic Press, New York, 1973.Google Scholar
  30. 30.
    B. R. Gaines, “On the Complexity of Causal Models,” IEEE Trans. on Systems, Man and Cybernetics, SMC-6, pp. 56–59, 1976.Google Scholar
  31. 31.
    A. Kolmogorov, “Logical Basis for Information Theory and Probability Theory,” IEEE Trans. Information Theory, IT-14, pp. 662–664, 1968.CrossRefGoogle Scholar
  32. 32.
    D. A. Ralescu, “Abstract Models for System Identification,” Faculte de Science Economique et de Gestion, Institut de Mathematiques Economiques, Dijon, France.Google Scholar
  33. 33.
    L. J. Savage, “Elicitation of Personal Probabilities and Their Assessment,” Journal American Statistical Association, 66, pp. 783–801.Google Scholar
  34. 34.
    B. de Finetti, Probability, Induction and Statistics, John Wiley, London, 1972.Google Scholar
  35. 35.
    J. Pearl, “An Economic Basis for Certain Methods of Evaluating Probabilistic Forecasts,” UCLA-ENG-7561, School of Engineering and Applied Science, UCLA, California, July 1975.Google Scholar
  36. 36.
    R. M. Wharton, “Approximate Language Identification,” Information and Control, 26, pp. 236–255, 1974.CrossRefGoogle Scholar
  37. 37.
    E. Sober, Simplicity, Clarendon Press, Oxford, 1975.CrossRefGoogle Scholar
  38. 38.
    J. V. Cornacchio, “System Complexity—A Bibliography,” International Journal of General Systems, 3, pp. 267–271, 1977.CrossRefGoogle Scholar
  39. 39.
    A. Van der Mude, and A. Walker, “On the Inference of Stochastic Regular Grammars,” Information and Control, to appear.Google Scholar
  40. 40.
    B. P. Zeigler, “Simulation Based Structural Complexity,” International Journal of General Systems, 2, pp. 217–223, 1976.CrossRefGoogle Scholar
  41. 41.
    R. M. Wharton, “Grammar Enumeration and Inference,” Information and Control, 33, pp. 253–272, 1977.CrossRefGoogle Scholar
  42. 42.
    G. J. Klir, “Identification of Generative Structures in Empirical Data,” International Journal of General Systems, 3, pp. 89–104, 1976.CrossRefGoogle Scholar
  43. 43.
    G. J. Klir, and H. J. J. Uyttenhove, “Computerized Methodology for Structure Modelling,” Annals of Systems Research, 5, pp. 29–66, 1976.CrossRefGoogle Scholar
  44. 44.
    G. J. Klir, and H. J. J. Uyttenhove, “On the Role of Computer-Aided Structure Identification: Some Experimental Observations and Guidelines,” International Journal of Man-Machine Studies, 9, 1977, to appear.Google Scholar
  45. 45.
    L. Blum, and M. Blum, “Toward a Mathematical Theory of Inductive Inference,” Information and Control, 28, pp. 125–155, 1975.CrossRefGoogle Scholar
  46. 46.
    S. Crespi-Reghizzi, M. A. Melankoff, and L. Lichten, “The use of Grammatical Inference for Designing Programming Languages,” Communications of Association for Computing Machinery, 16, pp. 83–90, 1973.CrossRefGoogle Scholar
  47. 47.
    A. Walker, “A Framework for Model Construction and Model-Based Deduction in Systems with Causal Loops,” Proceedings 3rd, Illinois Conf. on Med. Inf. Processing, Department of Information Engineering, University of Illinois at Chicago Circle, 1976.Google Scholar
  48. 48.
    I. H. Witten, “Non-Deterministic Modelling of Behaviour Sequences,” EES-MMS-MOD-77, April 1977, Department of Electrical Engineering Science, University of Essex.Google Scholar
  49. 49.
    J. H. Andreae, and J. G. Cleary, “A New Mechanism for the Brain,” International Journal of Man-Machine Studies, 8, pp. 89–119, 1976.CrossRefGoogle Scholar
  50. 50.
    P. Hajek, and T. Havranek, “On Generation of Inductive Hypotheses,” International Journal of Man-Machine Studies, 9, 1977, to appear.Google Scholar
  51. 51.
    Special issue on the GUHA method and its applications, International Journal of Man-Machine Studies, 10, January 1978.Google Scholar
  52. 52.
    R. Chilausky, B. Jacobson, and R. S. Michalski, “An Application of Variable-Valued Logic to Inductive Learning of Plant Disease Diagnostic Rules,” Proc. Sixth Int. Symp. on Multiple Valued Logic, IEEE 76 CH 1111–4C, Utah, May 1976, pp. 233–240.Google Scholar

Copyright information

© Springer Science+Business Media New York 1978

Authors and Affiliations

  • B. R. Gaines
    • 1
  1. 1.Man-Machine Systems Lab., Dept. of E. E. ScienceUniversity of EssexColchester, EssexUK

Personalised recommendations