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Portuguese Conference on Artificial Intelligence

EPIA 1997: Progress in Artificial Intelligence pp 297–302Cite as

A new approach for extracting rules from a trained neural network

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1323))

Abstract

Artificial Neural Networks perform adaptive learning. This advantage can be used to complete and improve the knowledge acquisition in knowledge engineering by rule extraction from a trained neural network. This paper proposes a new rule extraction method based on MACIE algorithm, which has been improved so that it can be used in neural networks with continuous inputs and outputs, obtaining a global and continuous set of production rules in a very efficient way. An application example to obtain the average load demand of a power plant is also shown.

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References

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Author information

Authors and Affiliations

  1. Departamento de Inteligencia Artificial Facultad de Informatica, Universidad Politecnica de Madrid, Campus de Montegancedo s/n, 28660, Boadilla de Monte - Madrid, Spain

    A. L. Castellanos & D. Manrique

  2. Escuela Universitaria de Ingenieros Técnicos Forestales, Universidad Politecnica de Madrid, Ciudad Universitaria s/n, 28040, Madrid, Spain

    J. Castellanos & A. Martinez

Authors
  1. A. L. Castellanos
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  2. J. Castellanos
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  3. D. Manrique
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  4. A. Martinez
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Editor information

Ernesto Coasta Amilcar Cardoso

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© 1997 Springer-Verlag Berlin Heidelberg

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Castellanos, A.L., Castellanos, J., Manrique, D., Martinez, A. (1997). A new approach for extracting rules from a trained neural network. In: Coasta, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 1997. Lecture Notes in Computer Science, vol 1323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0023931

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  • DOI: https://doi.org/10.1007/BFb0023931

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63586-4

  • Online ISBN: 978-3-540-69605-6

  • eBook Packages: Springer Book Archive

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