Model construction for quality of beer and brewing process using FNN

Models for sensory evaluation of beer and the beer brewing process were constructed using a fuzzy neural network (FNN). A new method for optimal model selection using a genetic algorithm and a SWEEP operator method was compared with a conventional method using the parameter increasing method. As the result, the new method was useful for the optimal model selection by simplifying the model structure, improving the reliability of fuzzy rules, and accelerating the calculation speed (about 10 times as fast as conventional method) for constructing the model with high accuracy. The percentage of correct answers of the sensory evaluation model is 92%. The important variables are selected as the input variables, and the obtained fuzzy rules in modeling coincide well with knowledge data bases acquired by process operators, and it is proven that the obtained FNN models are adequate.