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Intelligent systems modeling with reusable fuzzy objects
Author(s) -
Ndousse Thomas D.
Publication year - 1997
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/(sici)1098-111x(199702)12:2<137::aid-int2>3.0.co;2-r
Subject(s) - fuzzy set operations , fuzzy logic , fuzzy set , fuzzy classification , type 2 fuzzy sets and systems , artificial intelligence , computer science , knowledge base , defuzzification , neuro fuzzy , fuzzy number , fuzzy associative matrix , inference engine , vagueness , object (grammar) , adaptive neuro fuzzy inference system , data mining , mathematics , fuzzy control system
In this article, we present a formalism for embedding fuzzy logic into object‐oriented methodology in order to deal with the uncertainty and vagueness that pervade knowledge and object descriptions in the real world. We show how fuzzy logic can be used to represent knowledge in conventional objects, while still preserving the essential features of object‐oriented methodology. Fuzzy object attributes and relationships are defined and the framework for obtaining fuzzy generalizations and aggregations are formulated. Object's attributes in this formalism are viewed as hybrids of crisp and fuzzy characterizations. Attributes with vague descriptions are fuzzified and manipulated with fuzzy rules and fuzzy set operations, while others are treated as crisp sets. In addition to the fuzzification of the object's attributes, each object is provided with a fuzzy knowledge base and an inference engine. The fuzzy knowledge base consists of a set of fuzzy rules and fuzzy set operators. Objects with a knowledge base and an inference engine are referred to as intelligent objects. © 1997 John Wiley & Sons, Inc.