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Документ Fuzzy logic energy management system of food manufacturing processes(2020) Baliuta, Serhii; Kopylova, Liudmyla; Kuievda, Yulia; Kuyevda, Valery; Kovalchuk, OlenaIntroduction. The research is conducted to justify the method of increasing the efficiency of electrical power use in food production processes, which is based on the algorithms of fuzzy power management system. Materials and methods. The study is based on optimal and fuzzy control methods, such as Mamdani and Sugeno algorithms. Computer simulation was performed using MATLAB Simulink. Results and discussion. The control criterion of power supply of food production is formulated in the form of a functional minimization problem, which depends on the expected value of active and reactive power losses, active and reactive power losses at the level of secondary substations and individual load nodes. Maintaining energy efficient voltages in the power supply system nodes is chosen as a control task. It is determined the dependence of energy efficient voltages on the nominal voltage of the power system, the active resistance of its segments, the power and the coefficient of linearized static load characteristics at the network nodes, the equivalent active resistance, the nominal power, open circuit and short circuit losses of secondary substation transformers. To solve the control problem, an algorithm is synthesized using fuzzy controllers at the levels of the primary and secondary substations. In particular, it is determined that the input signals of the secondary substation fuzzy regulator should be the deviation from the energy efficient voltages and the rate of voltage change, and the output signals should be the transformer voltage and the actuation delay. Using numerical methods it is shown that this algorithm can reduce electricity losses in food production processes up to 7% compared to classical voltage regulation methods. Conclusions. The fuzzy system method under study ensures that energy-efficient voltage levels are maintained at the distribution network nodes when the voltage of the power source or the consumer loads are changed.Документ Автоматизована система управління хлібопекарським виробництвом(2013) Ельперін, Ігор Володимирович; Швед, Сергій МиколайовичРозглянуті питання розробки сучасної системи автоматизованого управління технологічним процесом виготовлення хліба з використанням інтелектуальних підсистем підтримки прийняття рішень. Підсистема підтримки прийняття рішення при виборі спеціальних добавок розроблена методами нечіткої логіки. Система оперативної корекции технологічних параметрів розроблена з використанням прогнозуючих моделей з використанням штучних нейронних мереж. The problems of the development of a modern system of process control production of bread using intelligent decision support subsystem. Subsystem for decision-making when choosing special additives developed methods of fuzzy logic. The system of surgical correction of process parameters is developed using predictive models using artificial neural networks.