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Identification of a nonlinear dependence by a fuzzy knowledgebase in the case of a fuzzy training set

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Cybernetics and Systems Analysis Aims and scope

An Erratum to this article was published on 01 September 2006

Abstract

This paper generalizes the method of identification of nonlinear dependences by a fuzzy knowledgebase to the case of fuzzy training sets. In such a set, terms are used to estimate inputs. The computer experiments performed showed that the fuzziness of experimental data is no obstacle to identification. The use of fuzzy training sets allows one to apply the proposed method to the identification of "input-output" dependences in medicine, economics, sociology, politics, and other areas in which experimental data are based on expert judgments.

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Authors and Affiliations

  1. Jerusalem Polytechnic Institute Makhon Lev, Jerusalem, Israel

    A. P. Rotshteina

  2. Vinnitsa National Technical University, Vinnitsa, Ukraine

    S. D. Shtovbab

Authors
  1. A. P. Rotshteina
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  2. S. D. Shtovbab
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Additional information

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Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 17–24, March–April 2006.

An erratum to this article is available at http://dx.doi.org/10.1007/s10559-006-0117-0.

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Rotshteina, A.P., Shtovbab, S.D. Identification of a nonlinear dependence by a fuzzy knowledgebase in the case of a fuzzy training set. Cybern Syst Anal 42, 176–182 (2006). https://doi.org/10.1007/s10559-006-0051-1

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  • DOI: https://doi.org/10.1007/s10559-006-0051-1

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