Efficient data-driven modeling with fuzzy relational rule network

Author

Gaweda, Adam E. ; Zurada, Jacek M. ; Aronhime, Peter B.

Author_Institution

Dept. of Electr. & Comput. Eng., Louisville Univ., KY, USA

Volume

1

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

174

Lastpage

178

Abstract

An algorithmic approach for efficient identification of a fuzzy relational rule network (FR²N) from data is presented. FR²N uses a relational input partition for human-understandable modeling of linear interactions between the input variables. Mutual subsethood has been used to estimate the optimal interaction structure. An analytical relationship between the mutual subsethood measure and one of the parameters of the membership functions is derived. The use of this relationship results in a dramatic speed-up of the identification process

Keywords

covariance matrices; fuzzy neural nets; fuzzy set theory; identification; probability; statistical analysis; algorithmic approach; data-driven modelling; fuzzy relational rule network; human-understandable modeling; identification; linear interactions; mutual subsethood; optimal interaction structure; relational input partition; Clustering algorithms; Covariance matrix; Electronic mail; Fuzzy neural networks; Fuzzy sets; Input variables; Linear approximation; Partitioning algorithms; Scattering parameters; Tree graphs;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2002. FUZZ-IEEE'02. Proceedings of the 2002 IEEE International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-7280-8

Type

conf

DOI

10.1109/FUZZ.2002.1004982

Filename

1004982