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Fuzziness and neural nets - merging the two approaches

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EN
Abstrakty
EN
This paper explores fuzzy neural trees and an approach for converting these trees into feed-forward neural network architectures. The proposed approach is unique in that it introduces the ways to use either technology as a "tool" within the framework of a model based on the other. It is of the highest significance as it results in new neural network algorithms where no time-consuming iterative training is required.
Rocznik
Strony
43--52
Opis fizyczny
Bibliogr. 23 poz.
Twórcy
Bibliografia
  • [1] Gupta M. M. and Qi J„ On Fuzzy Neuron Models, in: Fuzzy Logic for the Management of Uncertainty, Zadeh L. A. and Kacprzyk J., Eds., (John Wiley & Sons, Inc., New York, 1992)479-491.
  • [2] Ishibuchi H., Fujioka R., and Tanaka H., Possibility and Necessity Pattern Classification Using Neural Networks, Fuzzy Sets and Systems 48, (1992) 331-340.
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  • [17] Kung S. Y. and Hwang J. N., An Algebraic Projection Analysis for Optimal Hidden Units Size and Learning Rates in Back-Propagation Learning, in: Proceedings of IEEE International Conference on Neural Networks, (1988) 363-370.
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  • [19] Dietterich T. G., Hild H., and Bakiri G., A Comparative Study of 1D3 and Back- Propagation for English Text-to-Speech Mapping, in: Proceedings of the 7th International Conference on Machine Learning, Texas (1990).
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD7-0028-0029
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