Learning from examples for the construction of fuzzy evaluations

Author

Posthoff, Christian ; Schlosser, Michael

Author_Institution

Dept. of Comput. Eng., Chemnitz Univ. of Technol., Germany

fYear

1994

fDate

26-29 Jun 1994

Firstpage

368

Abstract

Many concepts of knowledge-based problem-solving are incorporated into one system which has been based on set-theoretic concepts. This results in a methodology and in a comprehensive set of tools applicable in many fields. The transition between fuzzy and non-fuzzy parts of the problem domain can be realized very flexibly, supplying a high smartness and high performance model. The paper has the intention to show how to unify many problem-solving methods on the basis of set theoretic concepts and how to handle precise and fuzzy notions concepts as flexible as possible

Keywords

Boolean functions; fuzzy logic; fuzzy set theory; knowledge based systems; learning by example; problem solving; Boolean functions; fuzzy evaluations; fuzzy set theory; knowledge-based problem-solving; learning from examples; uncertainty handling; Artificial intelligence; Chemical elements; Chemical technology; Computer science; Decision trees; Equations; Fuzzy sets; Knowledge based systems; Problem-solving; Prototypes;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1896-X

Type

conf

DOI

10.1109/FUZZY.1994.343670

Filename

343670