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Automatic generation of fuzzy logic rule bases: Example I

: Surmann, H.; Selenschtschikow, A.

Natural and Artificial Intelligence Systems Organization -NAISO-:
First International NAISO Congress on Neuro Fuzzy Technologies 2002. Program and proceeding. Buch : At 'El Capitolio' Conference Centre, Havana, Cuba, January 16 - 19, 2002
Havana: ICSC-NAISO Academic Press, 2002
ISBN: 3-906454-29-0
International Congress on Neuro Fuzzy Technologies (NF) <1, 2002, Havana>
Conference Paper
Fraunhofer AIS ( IAIS) ()
fuzzy logic controller; genetic algorithm; entropy of fuzzy rules; machine learning

Learning fuzzy rule-based systems with genetic algorithms can lead to very useful descriptions of several problems. Many different alternative descriptions can be generated. In many cases, a simple rule base similar to rule bases designed by humans is preferable since it has a higher possibility of being valid in unforeseen cases. Thus, the main idea of this paper is to study the genetic fuzzy rule base learning algorithm FRBL [1] by examples from the machine learning repository [2] and to compare it with some other approaches.