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fXOR fuzzy logic networks

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Abstract

 The study introduces a new class of fuzzy neurons and fuzzy neural networks exploiting a model of a generalized multivalued exclusive-OR (XOR) operation. The proposed neural architecture is useful in an algebraic representation (description) of fuzzy functions regarded as mappings between unit hypercubes, say [0,1]n→[0,1]m. Some underlying properties of the fXOR neurons are discussed and a detailed learning algorithm is given along with a number of illustrative numeric examples.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada T6G 2G7 e-mail: pedrycz@ee.ualberta.ca, , , , , , CA

    W. Pedrycz & G. Succi

Authors
  1. W. Pedrycz
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  2. G. Succi
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Additional information

The support from the Natural Sciences and Engineering Research Council of Canada (NSERC) and ASERC (Alberta Software Engineering Research Consortium) is gratefully acknowledged.

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Pedrycz, W., Succi, G. fXOR fuzzy logic networks. Soft Computing 7, 115–120 (2002). https://doi.org/10.1007/s00500-002-0179-5

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  • DOI: https://doi.org/10.1007/s00500-002-0179-5

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