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Recurrent fuzzy logic using neural network

  • Fuzzy — Neural Networks
  • Conference paper
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Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms (WWW 1994)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1011))

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Abstract

In this paper, a novel method is presented to combine neural nets with fuzzy logic. The combined technology is based on modified NeuFuz ([1], [2], [3]) using recurrent neural networks. The recurrent information of neural net is directly mapped to a new type of fuzzy logic, called “recurrent” fuzzy logic. Recurrency preserves temporal information and yields superior performance for context dependent applications. It also reduces the convergence time. Simulations show good improvements in accuracy and speed of convergence in pattern recognition applications.

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References

  1. E. Khan NeuFuz: An Intelligent Combination of Fuzzy Logic with Neural Nets, IJCNN, Nagoya,Oct93.

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Author information

Authors and Affiliations

  1. Intelligent Systems Group, Embedded Systems Division, National Semiconductor, 95052, Santa Clara, CA, USA

    Emdad Khan & Fatih Unal

Authors
  1. Emdad Khan
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  2. Fatih Unal
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Editor information

Takeshi Furuhashi

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© 1995 Springer-Verlag Berlin Heidelberg

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Khan, E., Unal, F. (1995). Recurrent fuzzy logic using neural network. In: Furuhashi, T. (eds) Advances in Fuzzy Logic, Neural Networks and Genetic Algorithms. WWW 1994. Lecture Notes in Computer Science, vol 1011. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60607-6_4

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  • DOI: https://doi.org/10.1007/3-540-60607-6_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60607-9

  • Online ISBN: 978-3-540-48457-8

  • eBook Packages: Springer Book Archive

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