A Multilayer Feedforward Fuzzy Neural Network

  • Aydoğan Savran
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3949)


This paper describes the architecture and learning procedure of a multilayer feedforward fuzzy neural network (FNN). The FNN is designed by replacing the sigmoid type activation function of the multilayer neural network (NN) with the fuzzy system (FS). The Levenberg-Marquardt (LM) optimization method with a trust region approach is adapted to train the FNN. Simulation results of a nonlinear system identification problem are given to show the validity of the approach.


Membership Function Hide Layer Activation Function Fuzzy System Fuzzy Rule 


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  1. 1.
    Yamada, T., Yabuta, T.: Neural Network Controller using Autotuning Method for Nonlinear Functions. IEEE Transactions on Neural Networks 3, 595–601 (1992)CrossRefGoogle Scholar
  2. 2.
    Chen, C.T., Chang, W.D.: A Feedforward Neural Network with Function Shape Auto-tuning. Neural Networks 9(4), 627–641 (1996)CrossRefGoogle Scholar
  3. 3.
    Guarnieri, S., Pizza, F.: Multilayer Feedforwardt Network with Adaptive Spline Activation Function. IEEE Transactions on Neural Networks 10(3), 672–683 (1999)CrossRefGoogle Scholar
  4. 4.
    Trentin, E.: Networks with Trainable Amplitude of Activation Functions. Neural Networks 14(4/5), 471–493 (2001)CrossRefGoogle Scholar
  5. 5.
    Olivas, E.S., Guerrero, J.D.M., Valls, G.C., Lopez, A.J.S., Maravilla, J.C., Chova, L.G.: A Low-Complexity Fuzzy Activation Function for Artificial Neural Networks. IEEE Transactions on Neural Networks 14(6), 1576–1579 (2003)CrossRefGoogle Scholar
  6. 6.
    Oysal, Y., Becerikli, Y., Konar, A.F.: Generalized modeling Principles of A Nonlinear System with a Dynamic Fuzzy Network. Computers & Chemical Engineering 27, 1657–1664 (2003)CrossRefMATHGoogle Scholar
  7. 7.
    Scales, L.E.: Introduction to Non-Linear Optimization, pp. 115–118. Springer, New York (1985)CrossRefGoogle Scholar
  8. 8.
    Wang, L.X.: A Course in Fuzzy Systems and Control. Prentice-Hall, Inc., Englewood Cliffs (1997)MATHGoogle Scholar
  9. 9.
    Ungar, L.H.: A Bioreactor Benchmark for Adaptive Network-based Process Control. In: Miller III, W.T., Sutton, R.S., Werbos, P.J. (eds.) Neural Networks for Control, pp. 387–402. MIT Press, London (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Aydoğan Savran
    • 1
  1. 1.Department of Electrical and Electronics EngineeringEge UniversityİzmirTurkey

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