A systematic approach to linguistic fuzzy modeling based on input-output data

Author

Salehfar, Hossein ; Bengiamin, Nagy ; Huang, Jun

Author_Institution

Dept. of Electr. Eng., North Dakota Univ., Grand Forks, ND, USA

Volume

1

fYear

2000

fDate

2000

Firstpage

480

Abstract

A novel systematic algorithm to build adaptive linguistic fuzzy models directly from input-output data is presented. Based on clustering and projection in the input and output spaces, significant inputs are selected, the number of clusters is determined, rules are generated automatically, and a linguistic fuzzy model is constructed. Then, using a simplified fuzzy reasoning mechanism, the back-propagation (BP) and least mean squared (LMS) algorithms are implemented to tune the parameters of the membership functions. Compared to other algorithms, the new algorithm is both computationally and conceptually simple. The new algorithm is called the Linguistic Fuzzy Inference (LFI) model

Keywords

adaptive systems; backpropagation; computational linguistics; digital simulation; fuzzy logic; fuzzy set theory; inference mechanisms; least mean squares methods; modelling; uncertainty handling; LFI model; Linguistic Fuzzy Inference model; adaptive linguistic fuzzy models; back-propagation; clustering; input-output data; least mean squared algorithm; linguistic fuzzy modeling; membership functions; parameter tuning; rule generation; simplified fuzzy reasoning mechanism; systematic algorithm; systematic approach; Clustering algorithms; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Humans; Inference algorithms; Iterative algorithms; Least squares approximation; Nonlinear equations;

fLanguage

English

Publisher

ieee

Conference_Titel

Simulation Conference, 2000. Proceedings. Winter

Conference_Location

Orlando, FL

Print_ISBN

0-7803-6579-8

Type

conf

DOI

10.1109/WSC.2000.899755

Filename

899755