Abstract
Automation of food production is an actively researched domain. One of the areas, where automation is still not progressing significantly is bread making. The process still relies on expert knowledge regarding how to react to procedure changes depending on environmental conditions, quality of the ingredients, etc. In this paper, we propose an ANFIS-based model for changing the mixer speed during the kneading process. Although the recipes usually indicate the time for which the mixing should be done using slow and fast mixing speeds, however, it is the human, who makes the final decision as the mixers differ in terms of the mixing quality, speed, etc. Furthermore, unexpected differences in flour quality or room conditions can impact the time required to mix the ingredients. In the paper, different methods for fuzzy modeling are described and analyzed. The tested models are compared using both generated and real data and the best solution is presented.
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Acknowledgments
The work has been supported partially by founds of the Department of Computer Architecture, Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology and the project POIR.01.01.01-00-1449/19-00 entitled “Opracowanie systemu, dedykowanego branży piekarniczej, pełniącego funkcje nadzorowania i sterowania powtarzalnym procesem produkcji ciasta”. The Authors would like to thank Joanna Woźna for her help in implementation of the models.
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Boiński, T., Szymański, J. (2023). Optimization of Bread Production Using Neuro-Fuzzy Modelling. In: Saeed, K., Dvorský, J., Nishiuchi, N., Fukumoto, M. (eds) Computer Information Systems and Industrial Management. CISIM 2023. Lecture Notes in Computer Science, vol 14164. Springer, Cham. https://doi.org/10.1007/978-3-031-42823-4_24
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