A Fuzzy Neural Network for Approximate Fuzzy Reasoning

  • Liam P. Maguire
  • T. Martin McGinnity
  • Liam J. McDaid


In this chapter the authors present an alternative neurofuzzy architecture for approximate fuzzy reasoning. The term approximate fuzzy reasoning is employed to highlight an approximation to the conventional fuzzy reasoning approach which considerably simplifies the resulting architecture. The performance of the fuzzy neural network is demonstrated by its application to three benchmark problems: nonlinear function approximation; on-line identification of control systems and finally chaotic time series prediction. Simulation results are presented using the MATLAB neural network toolbox and these are compared with traditional neural networks; other fuzzy neural networks and conventional fuzzy reasoning approaches. The work demonstrates the advantage of a neurofuzzy approach and highlights the advantages of this architecture for a hardware realization.


Hide Layer Fuzzy Neural Network Fuzzy Reasoning Chaotic Time Series Input Domain 
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.


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  1. [1]
    Berenji, H. R., Khedkar, P., “Learning and tuning fuzzy logic controllers through reinforcement,” IEEE Trans. Neural Networks 3:5, 724–740 (1992).CrossRefGoogle Scholar
  2. [2]
    Blake, J. J., Maguire, L. P., Roche, B., McGinnity, T. M., McDaid, L. J., “The Implementation of Fuzzy Systems, Neural Networks and Fuzzy Neural Networks Using FPGAs,” Accepted for publication at the Joint Conference on Information Sciences, USA (March 1997).Google Scholar
  3. [3]
    Box, G. E. P., Jenkins, G. M., Time series analysis: forecasting and control, Oakland C.A.: Holden-Day (1976).zbMATHGoogle Scholar
  4. [4]
    Brown, M., Harris, C., Neurofuzzy adaptive modelling and control, Prentice-Hall (1994).Google Scholar
  5. [5]
    Gupta, M. M., Rao, D. H., “On the principles of fuzzy neural networks,” Proc. Fuzzy Sets and Systems 61, 1–18 (1994).MathSciNetCrossRefGoogle Scholar
  6. [6]
    Horikawa, S., Furuhashi, T., Uchikawa, Y., “On fuzzy modelling using fuzzy neural networks with the back propagation algorithm,” IEEE Trans. Neural Nets 3:5, 801–806 (1992).CrossRefGoogle Scholar
  7. [7]
    Hunt, K. J., Sbarbaro, D., Zbikowski, R., Gawthrop, P. J., “Neural networks for control systems—a survey,” Automatica 28:6, 1083–1112 (1992).MathSciNetzbMATHCrossRefGoogle Scholar
  8. [8]
    Irwin, G. W., “Local models and local model networks,” Proc. EURACO Workshop, 213–223 (1996).Google Scholar
  9. [9]
    Jang, Roger J. S., “Predicting chaotic time series with fuzzy IF-THEN rules,” Proc. IEEE 2nd Int. Conf on Fuzzy Systems 2, 1079–1084 (1993).Google Scholar
  10. [10]
    Jang, Roger J. S., Sun, C.-T., “Neuro-fuzzy modelling and control,” Proc. of the IEEE 83:3, 378–406 (1995).CrossRefGoogle Scholar
  11. [11]
    Lapedes, A. S., Farber, R., Nonlinear signal processing using neural networks: prediction and system modelling, Technical report LA-UR-872662, Los Alamos National Laboratory, USA (1987).Google Scholar
  12. [12]
    Lee, C.C., “Fuzzy Logic in Control Systems: fuzzy Logic Controller—Parts I and II,” IEEE Trans. Systems Man and Cybernetics 20:2, 404–435 (1990).MathSciNetzbMATHCrossRefGoogle Scholar
  13. [13]
    Lin, C.T., Lee, C. S. G., “Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems,” IEEE Trans. Fuzzy Systems 2:1, 46–63 (1994).MathSciNetCrossRefGoogle Scholar
  14. [14]
    Linkens, D. A., Nyongesa, H. O., “Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications.” IEEE Proc. Control Theory Appl. 143:2, 367–386 (1996).zbMATHCrossRefGoogle Scholar
  15. [15]
    Mackey, M. C., Glass, L., “Oscillation and chaos in physiological control systems,” Science 197, 287–289 (1977).CrossRefGoogle Scholar
  16. [16]
    Maguire, L. P., Campbell, J. G., “Fuzzy reasoning using a three layer neural network,” Proc. Int Fuzzy Systems Association Conf 2, 627–631 (1995).Google Scholar
  17. [17]
    Maguire, L. P., Mc Ginnity, T. M., Hashim, A. A., Campbell, J. G., “On-Line Identification in Control Systems Using a Fuzzy Neural Network,” Proc. 4th European Congress on Intelligent Techniques and Soft Computing EUFIT’96 2, 742–746 (1996).Google Scholar
  18. [18]
    McGinnity, T. M., McDaid, L. J., Campbell, J. G., “Modelling Optimum Architectures for VLSI Implementations of Artificial Neural Networks,” Proc. Fifth Irish Neural Networks Conference, 143–151 (1995).Google Scholar
  19. [19]
    McGinnity, T. M., Maguire, L. P., McDaid, L. J., “A pseudo-parallel architecture for hardware implementations of neural networks,” Proc. 4th International Conference on Soft Computing IIZUKA’96 2, 722–725 (1996).Google Scholar
  20. [20]
    Mead, C. A., Analog VLSI and neural systems, Addison-Wesley (1989).Google Scholar
  21. [21]
    Mitra S., Pal, S. K., “Logical operation based fuzzy MLP for classification and rule generation,” Neural Networks 7:2, 353–373 (1994).CrossRefGoogle Scholar
  22. [22]
    Moody, J., Darken, C., “Fast learning in networks of locally tuned processing units,” Neural Computation 1, 281–294 (1989).CrossRefGoogle Scholar
  23. [23]
    Narendra, K. S., Parthasarathy, K., “Identification and control of dynamical systems using neural networks,” IEEE Trans. on Neural Networks 1:1, 4–27 (1990).CrossRefGoogle Scholar
  24. [24]
    Nie, J., Linkens, D., “Neural network-based approximate reasoning: principles and implementation,” Int. Journal of Control 56:2, 399–413 (1992).MathSciNetzbMATHCrossRefGoogle Scholar
  25. [25]
    Pedrycz, W., “Fuzzy neural networks with reference neurons as pattern classifiers,” IEEE Trans. Neural Nets 3:5, 770–775 (1992).CrossRefGoogle Scholar
  26. [26]
    Roche, B.L., McGinnity, T. M., Maguire, L. P., McDaid, L. J., “Modelling architectures for VLSI implementations of fuzzy logic systems,” Accepted for publication at the Joint Conference on Information Sciences, USA (March 1997).Google Scholar
  27. [27]
    Simpson, P. K., Jahns, G., “Fuzzy min-max neural networks for function approximation,” Proc. IEEE Int. Conf. on Neural Networks 3, 1967–1972 (1993).CrossRefGoogle Scholar
  28. [28]
    Sugeno, M., Kang, G. T., “Structure identification of fuzzy model,” Fuzzy Sets and Systems 28, 15–33 (1988).MathSciNetzbMATHCrossRefGoogle Scholar
  29. [29]
    Takagi, T., Sugeno, M., “Fuzzy identification of systems and its application to modelling and control,” IEEE Trans. Systems Man and Cybernetics 15, 116–132 (1985).zbMATHCrossRefGoogle Scholar
  30. [30]
    Takagi, H., “Fusion Technology of Fuzzy Theory and Neural Networks: Survey and Future Directions,” Proc. Int. Conf. on Fuzzy Logic and Neural Networks, 13–26 (July 1990).Google Scholar
  31. [31]
    Takagi, T., Hayashi, I., “NN-Driven fuzzy reasoning,” Int. Journ. of Approx. Reasoning 5, 191–212 (1991).zbMATHCrossRefGoogle Scholar
  32. [32]
    Wang, L.X., Mendel, J.M., “Generating fuzzy rules by learning from examples,” IEEE Trans. Systems Man and Cybernetics 22:6, 1414–1427 (1992).MathSciNetCrossRefGoogle Scholar
  33. [33]
    Werntges, H. W., “Partitions of unity improve neural function approximation,” Proc. IEEE Int. Conf on Neural Networks, 914–918 (1993).Google Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Liam P. Maguire
    • 1
  • T. Martin McGinnity
    • 1
  • Liam J. McDaid
    • 1
  1. 1.Intelligent Systems Engineering Laboratory Faculty of EngineeringUniversity of UlsterDerryUK

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