Model construction for quality of beer and brewing process using FNN

Models for sensory evaluation of beer and the beer brewing process were con structed using a fuzzy neural network (FNN). A new method for optimal model selection using a genetic algorithm and a SWEEP operator method was compar ed with a conventional method using the parameter increasing method. As the result, the new method was useful for the optimal model selection by simpl ifying the model structure, improving the reliability of fuzzy rules, and a ccelerating the calculation speed (about 10 times as fast as conventional m ethod) for constructing the model with high accuracy. The percentage of cor rect answers of the sensory evaluation model is 92%. The important variable s are selected as the input variables, and the obtained fuzzy rules in mode ling coincide well with knowledge data bases acquired by process operators, and it is proven that the obtained FNN models are adequate.