Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Savran, A. (2006). A Multilayer Feedforward Fuzzy Neural Network. In: Savacı, F.A. (eds) Artificial Intelligence and Neural Networks. TAINN 2005. Lecture Notes in Computer Science(), vol 3949. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11803089_9
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DOI: https://doi.org/10.1007/11803089_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36713-0
Online ISBN: 978-3-540-36861-8
eBook Packages: Computer ScienceComputer Science (R0)