Neural Networks in Manufacturing
Tóm tắt
Attention focuses on minimizing the time to train a neural network so that it recognizes a specified set of a system’s input parameters. In training the neural network, the error function must be minimized. This is important in expert assessment of solutions generated by a smart system for the design of manufacturing processes. In such a system, solutions are generated by the combined operation of numerous modules on the basis of logical rules. The system to be designed will generally be complex and may contain subsystems of different types that function according rules described by fuzzy logic and systems of precedents [1].
Tài liệu tham khảo
Simonova, L., Egorova, E., and Akhmadiev, A., Knowledge acquisition for engineering decisions based on functional relationships, Int. J. Emerging Trends Eng. Res., 2020, vol. 91, pp. 2774–2778.
Simonova, L.A. and Egorova, E.I., Modular representation of the product in the knowledge base in the technological process formation, Int. Sci. Conf., 2015, no. 258, pp. 6537–6540.
Egorova, E.I., Representation of information about part on the basis of its engineering features international, J. Innovative Technol. Explor. Eng., 2019, vol. 8, no. 12, pp. 4084–4089.
Solomka, Yu.I., Application of genetic algorithms for training neural networks, Materialy IV studencheskoi nauchno-prakticheskoi konferentsii “Mir molodezhi— molodezh’ mira” (Proc. 4th Student Sci.-Pract. Conf. “The World of Youth—the Youth of the World”), Vinnitsa: Vinnits. Inst. MAUP, 2004, ch. 1, pp. 85–90.
Haykin, S., Neural Networks. A Comprehensive Foundation, Upper Saddle River, N.J.: Prentice Hall, 1998.