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 operato r all through the process are implicitly contained in such a database, as w ell as the recorded consequences on the product. The aim of this work is kn owledge discovery. This knowledge must be expressed in a way that allows co operation with the expert's knowledge. The system is implemented as a self- learning fuzzy controller, with the rule conclusions being optimized by a g enetic algorithm. The role of the fuzzy controller architecture is to provi de a learning framework, the database being used for rule validation, thus acquiring hidden knowledge. In order to make inferred knowledge easy to und erstand, a rule and variable selection methodology has been developed. Data from a cheesemaking process were used to test our approach. (C) 2001 Elsev ier Science B.V. All rights reserved.