Deriving prediction intervals for neurofuzzy networks

  • Authors:

  • G. Castellano;A. M. Fanelli;C. Mencar

  • Affiliations:

  • CILAB - Computational Intelligence LABoratory, Department of Computer Science, University of Bari, v. E. Orabona, 4 - 70126 - Bari, Italy;CILAB - Computational Intelligence LABoratory, Department of Computer Science, University of Bari, v. E. Orabona, 4 - 70126 - Bari, Italy;CILAB - Computational Intelligence LABoratory, Department of Computer Science, University of Bari, v. E. Orabona, 4 - 70126 - Bari, Italy

  • Venue:
  • ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
  • Year:
  • 2003

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Abstract

In this paper, we describe a method to calculate prediction intervals for neurofuzzy networks used as predictive systems. The method also allows defining prediction intervals for the fuzzy rules that constitute the rule base of the neuro-fuzzy network, resulting in a more readable knowledge base. Moreover, the method does not depend on a specific architecture and can be applied to a variety of neuro-fuzzy models. An illustrative example is reported to show the validity of the proposed approach.