Pattern recognition for holonic manufacturing systems

Author

Ferariu, L. ; Panescu, D.

Author_Institution

Dept. of Autom. Control & Appl. Inf., “Gh. Asachi” Tech. Univ. of Iasi, Iasi, Romania

fYear

2009

fDate

23-26 Aug. 2009

Firstpage

1257

Lastpage

1262

Abstract

The paper addresses to the pattern recognition problem within the framework of holonic manufacturing systems. A novel methodology with enhanced adaptation capabilities devoted to features extraction is suggested. It involves a flexible genetic selection of relevant features, in accordance with the specific properties of the patterns that have to be recognized. The competing features are determined by means of principal component analysis, bi-dimensional Fourier transformation and grey-levels analysis. The problem is formulated as a multi-objective optimisation, addressing to both classification accuracy and parsimony. The experimental results reveal the improvement of overall performances of the pattern recognition subsystem.

Keywords

Fourier transforms; feature extraction; grey systems; manufacturing systems; optimisation; pattern recognition; principal component analysis; bidimensional Fourier transformation; classification accuracy; features extraction; flexible genetic selection; grey-levels analysis; holonic manufacturing system; multiobjective optimisation; parsimony; pattern recognition problem; principal component analysis; Feature extraction; Genetics; Histograms; Optimization; Pattern recognition; Principal component analysis; Sociology;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 2009 European

Conference_Location

Budapest

Print_ISBN

978-3-9524173-9-3

Type

conf

Filename

7074578