Knowledge discovery for control purposes in food industry ... .fr
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Knowledge discovery for control purposes in food industry databases Serge Guillaumea; ∗ , Brigitte Charnomordicb a Cemagref
Laboratoire GIQUAL, 361 rue Jean-Francois Breton, 34033 Montpellier, France Laboratoire de Biom'etrie, 2 Place Viala, 34060 Montpellier, France
b INRA
Received 6 January 1999; received in revised form 22 December 1999; accepted 23 May 2000
Abstract Sets of experimental data describing a product at various processing steps are widely available in food industry. Decisions taken by the human operator all through the process are implicitly contained in such a database, as well as the recorded consequences on the product. The aim of this work is knowledge discovery. This knowledge must be expressed in a way that allows cooperation with the expert’s knowledge. The system is implemented as a self-learning fuzzy controller, with the rule conclusions being optimized by a genetic algorithm. The role of the fuzzy controller architecture is to provide a learning framework, the database being used for rule validation, thus acquiring hidden knowledge. In order to make inferred knowledge easy to understand, a rule and variable selection methodology has been developed. Data from a cheesemaking c 2001 Elsevier Science B.V. All rights reserved. process were used to test our approach. Keywords: Fuzzy logic; Genetic algorithm; Machine learning; Knowledge discovery; Process control; Food industry; Cheesemaking
1. Introduction Food industry involves many strongly non linear multivariable complex processes. Control of processes by classical methods is based on model use. But, for complex and imperfectly understood processes, phenomena are not su
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