GENETIC FUZZY LEARNING SYSTEMS BASED ON RULES

Authors

  • Наталія Анатоліївна Новосьолова Bioinformatic Department of the United Institute of Informatic Problems of the National Academy, of Sciences of Belarus
  • Ігор Едуардович Том Bioinformatic Department of the United Institute of Informatic Problems of the National Academy, of Sciences of Belarus

Keywords:

Fuzzy system, the knowledge base, the genetic learning

Abstract

The paper presents the general scheme of the evolutionary learning of the rule-based fuzzy systems, where the genetic procedures are used for the learning and tuning the different system components. Several schemes for the genetic tuning of the data base, genetic learning of the rule base and two variants for the fuzzy system’ knowledge-base learning are proposed. The problem of the interpretability of the fuzzy system is considered.

Author Biographies

Наталія Анатоліївна Новосьолова, Bioinformatic Department of the United Institute of Informatic Problems of the National Academy, of Sciences of Belarus

Ph.D., Senior Researcher of the Bioinformatic Department of the United Institute of Informatic Problems of the National Academy, of Sciences of Belarus

Ігор Едуардович Том, Bioinformatic Department of the United Institute of Informatic Problems of the National Academy, of Sciences of Belarus

Ph.D., a Head of the Bioinformatic Department of the United Institute of Informatic Problems of the National Academy, of Sciences of Belarus

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Published

2012-12-10

Issue

Vol. 9 No. 1 (2012)

Section

DATABASES AND SOFTWARE ENGINEERING