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Development of Fuzzy Neural Networks

  • Chapter
Fuzzy Modelling

Part of the book series: International Series in Intelligent Technologies ((ISIT,volume 7))

Abstract

In this paper, we explain how multi-layer feedforward neural networks can be fuzzified by using fuzzy numbers for inputs, targets and connection weights. First we briefly review a standard three-layer feedforward neural network and its back-propagation learning algorithm. Next we fuzzify the neural network by extending its inputs, targets and connection weights to fuzzy numbers. The input-output relation of each unit of the fuzzified neural network is defined by the extension principle of Zadeh. We also describe how a learning algorithm of the fuzzified neural network can be derived in a similar manner to the back-propagation algorithm. Finally, we illustrate possible application areas of the fuzzified neural network by simple examples.

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Authors and Affiliations

  1. Department of Industrial Engineering, University of Osaka Prefecture, Gakuen-cho 1-1, Sakai, Osaka, 593, Japan

    Hisao Ishibuchi

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  1. Hisao Ishibuchi
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Editors and Affiliations

  1. Electrical and Computer Engineering, University of Manitoba, Winnipeg, R3T 2N2, Canada

    Witold Pedrycz

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© 1996 Kluwer Academic Publishers

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Ishibuchi, H. (1996). Development of Fuzzy Neural Networks. In: Pedrycz, W. (eds) Fuzzy Modelling. International Series in Intelligent Technologies, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-1365-6_9

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  • DOI: https://doi.org/10.1007/978-1-4613-1365-6_9

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4612-8589-2

  • Online ISBN: 978-1-4613-1365-6

  • eBook Packages: Springer Book Archive

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