Fuzzy Model Identification of a Biological Process Based on Input-Output Data Clustering

Author

Grisales, Victor Hugo ; Gauthier, Alain ; Roux, Gilles

Author_Institution

Lab. LAAS-CNRS, Univ. Paul Sabatier, Toulouse

fYear

2005

fDate

25-25 May 2005

Firstpage

927

Lastpage

932

Abstract

This paper deals with the application of fuzzy clustering techniques for the model identification of an enhanced FAMIMO biological wastewater treatment process from input-output data. This MIMO system is represented as a set of coupled MISO models of the Takagi-Sugeno type. A comparative study with two clustering algorithms for the construction of the fuzzy model is carried out. The Gustafson-Kessel (GK) algorithm and the so-called robust parallel competitive agglomerative (RPCA) algorithm are considered. From a biotechnological point of view, different simulation experiences were conducted integrating both continuous and batch modes in order to validate the obtained models. Results are reported and discussed

Keywords

MIMO systems; fuzzy control; fuzzy set theory; nonlinear systems; parallel algorithms; pattern clustering; robust control; wastewater treatment; FAMIMO biological wastewater treatment process; Gustafson-Kessel algorithm; MIMO system; MISO models; Takagi-Sugeno type models; batch mode; biological process; biotechnological processes; continuous mode; fuzzy clustering technique; fuzzy model identification; input-output data clustering; nonlinear identification; robust parallel competitive agglomerative algorithm; Biological control systems; Biological processes; Biological system modeling; Clustering algorithms; Laboratories; MIMO; Mathematical model; Robustness; Takagi-Sugeno model; Wastewater treatment;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Conference_Location

Reno, NV

Print_ISBN

0-7803-9159-4

Type

conf

DOI

10.1109/FUZZY.2005.1452518

Filename

1452518