Flow Graph Approach for Studying Fuzzy Inference Systems | Zendy

Alicja Mieszkowicz-Rolka | Zendy; Leszek Rolka | Zendy

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Flow Graph Approach for Studying Fuzzy Inference Systems

Author(s) -

Alicja Mieszkowicz-Rolka,

Leszek Rolka

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.08.150

Subject(s) - computer science , adaptive neuro fuzzy inference system , inference , fuzzy set operations , fuzzy number , fuzzy logic , graph , fuzzy inference , fuzzy classification , type 2 fuzzy sets and systems , data mining , fuzzy set , fuzzy control system , artificial intelligence , theoretical computer science

This paper proposes an approach to analysis of fuzzy inference systems. To this end, a flow graph representation of a decision table with linguistic values is used. Every layer in a fuzzy flow graph corresponds to a fuzzy attribute (input or output). Each node in a layer represents a particular linguistic value of an attribute. Certainty factor, coverage factor and strength of rules can be computed for a given set of examples (universe) and serve as helpful measures for gaining more insight into the operation of a fuzzy inference system. Moreover, application of flow graphs can be useful for selecting membership functions and determining other parameters in the design of fuzzy inference systems

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