Model construction for quality of beer and brewing process using FNN

Hideki Noguchi, Taizo Hanai, Wataru Takahashi, Tomohiko Ichii, Mitsuru Tanikawa, Susumu Masuoka, Hiroyuki Honda, Takeshi Kobayashi

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)700-701
Number of pages2
Journalkagaku kogaku ronbunshu
Volume25
Issue number5
Publication statusPublished - Sep 1 1999
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Chemical Engineering(all)

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  • Cite this

    Noguchi, H., Hanai, T., Takahashi, W., Ichii, T., Tanikawa, M., Masuoka, S., Honda, H., & Kobayashi, T. (1999). Model construction for quality of beer and brewing process using FNN. kagaku kogaku ronbunshu, 25(5), 700-701.