TSK-Based Linguistic Fuzzy Model with Uncertain Model Output

Keun-Chang KWAK  Dong-Hwa KIM  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E89-D   No.12   pp.2919-2923
Publication Date: 2006/12/01
Online ISSN: 1745-1361
DOI: 10.1093/ietisy/e89-d.12.2919
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Computation and Computational Models
Keyword: 
TSK-type linguistic fuzzy model,  uncertain output,  context-based fuzzy clustering,  bias term,  water purification plant,  

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Summary: 
We present a TSK (Takagi-Sugeno-Kang)-based Linguistic Fuzzy Model (TSK-LFM) with uncertain model output. Based on the Linguistic Model (LM) proposed by Pedrycz, we develop a comprehensive design framework. The main design process is composed of the automatic generation of the contexts, fuzzy rule extraction by Context-based Fuzzy C-Means (CFCM) clustering, connection of bias term, and combination of TSK and linguistic context. Finally, we contrast the performance of the presented models with other models for coagulant dosing process in a water purification plant.