Abstract
In the last years the connectionist approaches have been successfully applied in several areas like image processing, speech recognition, pattern recognition and signal processing. Lately some attempts have been made in order to permit the neural networks to process and to manage uncertain information. A promising strategy is the use of the fuzzy logic, which has been successfully applied for the management of uncertainty and imprecision. Several authors have combined the fuzzy logic with tlle connectionist approach by different point of view (Hayashi et al., 1993, Ishibuchi et al., 1993, Keller and Tahani, 1992, Pedrycz, 1993). The main purpose of this study is to investigate an architecture in which each node is able to process fuzzy sets. The Simplicity of the back-propagation algorithm has been preserved by the use of normalized trapezoidal fuzzy sets.
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© 1994 Springer-Verlag London Limited
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Bortolan, G. (1994). Neural Networks for the Processing of Fuzzy Sets. In: Marinaro, M., Morasso, P.G. (eds) ICANN ’94. ICANN 1994. Springer, London. https://doi.org/10.1007/978-1-4471-2097-1_42
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DOI: https://doi.org/10.1007/978-1-4471-2097-1_42
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