Knowledge discovery for control purposes in food industry databases
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 process were used to test our approach. (C) 2001 Elsevier Science B.V. All rights reserved.
knowledge discovery food industry genetic algorithm cheesemaking fuzzy logic machine learning process control