Knowledge-based fuzzy neural networks

  • Les M. Sztandera
Communications Session 4a Approximate Resoning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1079)


Knowledge-based neural networks are concerned with the use of numerical information, which forms the domain knowledge, obtained from sensor measurements to determine the initial structure of a neural network. Research on combining symbolic inductive learning with neural networks, as well as research on combining fuzzy logic with neural networks, is proceeding on several fronts. Fuzzy decision trees and their various algorithmic implementations are one of the most popular choices in applications to learning and reasoning from feature-based examples. Such constructions have drawn increasing attention recently due to comprehensibility of the generated knowledge structure, and wide availability of data in the form of feature descriptions. However, the inability of coping with missing data, imprecise or vague information, and measurements errors create a lot of problems for symbolic artificial intelligence. These problems might be overcome by employing fuzzy methodology. In this paper we present an approach based on fuzzy neural trees for determining the structure of a neural network. An analysis of digital thallium-201 myocardial scintigraphs is presented to corroborate the theory and demonstrate the utility of the approach.


Learning and Knowledge Discovery Knowledge-Based Neural Networks Neural Heuristics 


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  1. [1]
    P. Lippmann, An Introduction to Computing with Neural Nets, IEEE Acoustics, Speech, and Signal Processing 44 (1987) 4–22.Google Scholar
  2. [2]
    B. Irie and S. Miyake, Capabilities of Three-Layered Perceptrons, in: Proc. of IEEE Inter. Conf. on Neural Networks, (1988) 641–648.Google Scholar
  3. [3]
    S. Y. Kung and J. N. Hwang, An Algebraic Projection Analysis for Optimal Hidden Units Size and Learning Rates in Back-Propagation Learning, in: Proc. of IEEE Int. Conf. on Neural Networks, (1988) 363–370.Google Scholar
  4. [4]
    J. A. Sirat and J. P. Nadal, Neural Trees: a New Tool For Classification, Network 1 (1990) 423–438.Google Scholar
  5. [5]
    M. Bichsel and P. Seitz, Minimum Class Entropy: A Maximum Information Approach To Layered Networks, Neural Networks 2 (1989) 133–141.Google Scholar
  6. [6]
    J. R. Quinlan, Induction of Decision Trees, Machine Learning 1 (1986) 81–106.Google Scholar
  7. [7]
    T. G. Dietterich, H. Hild, and G. Bakiri, A Comparative Study of ID3 and Back-Propagation for English Text-to-Speech Mapping, in: Proc. of the 7th Int. Conference on Machine Learning, Texas (1990).Google Scholar
  8. [8]
    D. H. Fisher and K. B. Mckusick, An Empirical Comparison of ID3 and Back-Propagation, in: Proc. of the 11th Int. Conf. on AI, (1989) 788–793.Google Scholar
  9. [9]
    K. J. Cios and N. Liu, A Machine Learning Method for Generation of a Neural Network Architecture: A Continuous ID3 Algorithm, IEEE Neural Networks 2 (1992) 280–291.Google Scholar
  10. [10]
    S. E. Fahlman and C. Labiere, The Cascade-Correlation Learning Architecture, in Advances in Neural Information Processing Systems 2, D. S. Touretzky, Ed., (Morgan Kaufmann Publishers, Los Altos, 1990) 524–532.Google Scholar
  11. [11]
    L. M. Sztandera, Dynamically Generated Neural Network Architectures, J. of Artificial Neural Systems 1 (1994) 41–66.Google Scholar
  12. [12]
    L. A. Zadeh, Fuzzy Sets, Information and Control 8, (1965) 338–353.Google Scholar
  13. [13]
    A. D. Nelson, R. F. Leighton, L. T. Andrews, L. S. Goodenday, L. Yonovitz, and D. Thekdi, A Comparison of Methods for the Analysis of Stress Thallium-201 Scintigraphs, in: Proc. of the Comp. in Cardiology Conference, (1979) 315–318.Google Scholar
  14. [14]
    M. M. Gupta and J. Qi, On Fuzzy Neuron Models, in: Fuzzy Logic for the Management or Uncertainty, L. A. Zadeh and J. Kacprzyk, Eds., (John Wiley & Sons, Inc., New York, 1992) 479–491.Google Scholar
  15. [15]
    H. Ishibuchi, R. Fujioka, and H. Tanaka, Possibility and Necessity Pattern Classification Using Neural Networks, Fuzzy Sets and Systems 48, (1992) 331–340.Google Scholar
  16. [16]
    H. Ishibuchi, R. Fujioka, and H. Tanaka, Neural Networks that Learn from Fuzzy If-Then Rules, IEEE Fuzzy Systems 1 (2), (1993) 85–97.Google Scholar
  17. [17]
    J. M. Keller and H. Tahani, Backpropagation Neural Networks for Fuzzy Logic, Information Sciences 62, (1992) 205–221.Google Scholar
  18. [18]
    J. M. Keller, R. R. Yager, and H. Tahani, Neural Network Implementation of Fuzzy Logic, Fuzzy Sets and Systems 45, (1992) 1–12.Google Scholar
  19. [19]
    K. Hirota and W. Pedrycz, Fuzzy Logic Neural Networks: Design and Computations, in: Proc. of the IJCNN'91 (2), Singapore, (1991) 1588–1593.Google Scholar
  20. [20]
    Y. Hayashi, E. Czogala, and J. J. Buckley, Fuzzy Neural Controller, in: Proc. of 1st Int. Conference on Fuzzy and Neural Systems, San Diego, (1992) 197–202.Google Scholar
  21. [21]
    B. Kosko, Neural Networks and Fuzzy Systems, (Prentice Hall, Englewood Cliffs, 1992).Google Scholar
  22. [22]
    L. X. Wang and J. M. Mendel, Generating Fuzzy Rules by Learning from Examples, IEEE Systems, Man, and Cybernetics 22 (6), (1992) 1414–1427.Google Scholar
  23. [23]
    K. Hornik, Approximation Capabilities of Multilayer Feedforward Networks, Neural Networks 4, (1991) 251–257.Google Scholar
  24. [24]
    L. X. Wang, Fuzzy Systems are Universal Approximators, in: Proc. of 1st Int. 1 Conference on Fuzzy and Neural Systems, San Diego, (1992) 1163–1169.Google Scholar
  25. [25]
    J. Dombi, A General Class of Fuzzy Operators, the De Morgan Class of Fuzzy Operators and Fuzziness Measures, Fuzzy Sets and Systems 8, (1982) 149–163.Google Scholar
  26. [26]
    H. Szu and R. Hartley, Fast Simulated Annealing, Phys. Lett. A 122 (8) (1987) 157–162.Google Scholar
  27. [27]
    R. R.Yager, On Choosing Between Fuzzy Subsets, Kybernetes 9, (1980) 151–154.Google Scholar
  28. [28]
    S. Murakami, H. Maeda and S. Immamura, Fuzzy Decision Analysis on the Development of Centralized Regional Energy Control System, in: Preprints of IFAC Conference on Fuzzy Information, Knowledge Representation and Decision Analysis, (1983) 353–358.Google Scholar
  29. [29]
    L. M. Sztandera, A Comparative Study of Ranking Fuzzy Sets Defined by a Neural Network Algorithm — Justification for a Centroidal Method, Archives of Control Sciences 4 (1/2), (1995) 89–111.Google Scholar
  30. [30]
    L. M. Sztandera, Fuzzy Neural Trees, Information Sciences 90 (1/4), (1996) 155–177.Google Scholar
  31. [31]
    K. J. Cios, L.S. Goodenday, and L. M. Sztandera, Hybrid Intelligence Systems or Diagnosing Coronary Stenosis — Combining Fuzzy Generalized Operators with Decision Rules Generated by Machine Learning Algorithms, IEEE Engineering in Medicine and Biology 13 (5), (1994) 723–729.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Les M. Sztandera
    • 1
  1. 1.Computer Science DepartmentPhiladelphia College of Textiles and SciencePhiladelphiaUSA

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