title: Multi-criterion optimization of control process based on DPCA and ANN
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: ÐÐ½Ð¾Ð³Ð¾ÐºÑÐ¸ÑÐµÑÐ¸Ð°Ð»ÑÐ½Ð°Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ° ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ DPCA Ð¸ ÐÐÐ¡
Creators: name: Ð§Ð°Ð²ÐµÐ»Ð¸Ð¿Ð°ÑÐ°Ð¼Ð±Ð¸Ð» ÐÐ¶Ð°ÑÐ½, Ð¯Ð´ÑÑÐºÑÐ¸ÑÐ½Ð°Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÐµ Ð¿ÑÐµÐ´Ð¿ÑÐ¸ÑÑÐ¸Ñ
principal component analysis
multicriteria optimization
genetic algorithm for non-dominated sorting
dynamic principal component analysis
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Ð¼Ð½Ð¾Ð³Ð¾ÐºÑÐ¸ÑÐµÑÐ¸Ð°Ð»ÑÐ½Ð°Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
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description: The modern approach to managing industrial systems assumes that the most important indicators such as productivity, material costs and environmental impact must be optimized simultaneously. It is advisable to perform optimization based on the analysis of big data contained in the databases of production systems. A multicriteria optimization is proposed using a neural network model of an industrial system and a non-dominant genetic sorting algorithm NSGA-II. Dynamic principal component analysis is an extension of Principal component analysis and used to reduce the dimensionality of industrial system to higher to lower dimension.Â  DPCA helps to capture the auto correlative behaviour of the parameters in industrial system, and it uses the time shifted replicates of the parameters. The main design aspect of DPCA includes the selection of lags and components for final model. The ANN model using DPCA method and NSGA-II algorithm allow us to find more efficient solution to the multicriteria optimization of the industrial system.Â  The method helps to optimize the process in industrial systems, and systems having large number parameters.
description: Ð¡Ð¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐ¹ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ðº ÑÐ¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÐ¼Ð¸ ÑÐ¸ÑÑÐµÐ¼Ð°Ð¼Ð¸ Ð¿ÑÐµÐ´Ð¿Ð¾Ð»Ð°Ð³Ð°ÐµÑ, ÑÑÐ¾ Ð²Ð°Ð¶Ð½ÐµÐ¹ÑÐ¸Ðµ Ð¿Ð¾ÐºÐ°Ð·Ð°ÑÐµÐ»Ð¸, ÑÐ°ÐºÐ¸Ðµ ÐºÐ°Ðº Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ, Ð¼Ð°ÑÐµÑÐ¸Ð°Ð»ÑÐ½ÑÐµ Ð·Ð°ÑÑÐ°ÑÑ Ð¸ Ð²Ð¾Ð·Ð´ÐµÐ¹ÑÑÐ²Ð¸Ðµ Ð½Ð° Ð¾ÐºÑÑÐ¶Ð°ÑÑÑÑ ÑÑÐµÐ´Ñ, Ð´Ð¾Ð»Ð¶Ð½Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑÑÑ Ð¾Ð´Ð½Ð¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾. Ð¦ÐµÐ»ÐµÑÐ¾Ð¾Ð±ÑÐ°Ð·Ð½Ð¾ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð±Ð¾Ð»ÑÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ, ÑÐ¾Ð´ÐµÑÐ¶Ð°ÑÐ¸ÑÑÑ Ð² Ð±Ð°Ð·Ð°Ñ Ð´Ð°Ð½Ð½ÑÑ Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´ÑÑÐ²ÐµÐ½Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼. ÐÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÑÑÑ Ð¼Ð½Ð¾Ð³Ð¾ÐºÑÐ¸ÑÐµÑÐ¸Ð°Ð»ÑÐ½Ð°Ñ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¸ Ð½ÐµÐ´Ð¾Ð¼Ð¸Ð½Ð°Ð½ÑÐ½Ð¾Ð³Ð¾ Ð³ÐµÐ½ÐµÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐ¾ÑÑÐ¸ÑÐ¾Ð²ÐºÐ¸ NSGA-II. ÐÐ¸Ð½Ð°Ð¼Ð¸ÑÐµÑÐºÐ¸Ð¹ Ð³Ð»Ð°Ð²Ð½ÑÐ¹ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ½ÑÐ¹ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°ÑÑÐ¸ÑÐµÐ½Ð¸ÐµÐ¼ Ð³Ð»Ð°Ð²Ð½Ð¾Ð³Ð¾ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑÑÑ Ð´Ð»Ñ ÑÐ½Ð¸Ð¶ÐµÐ½Ð¸Ñ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ñ Ð±Ð¾Ð»ÑÑÐµÐ¹ Ðº Ð¼ÐµÐ½ÑÑÐµÐ¹ ÑÐ°Ð·Ð¼ÐµÑÐ½Ð¾ÑÑÐ¸. DPCA Ð¿Ð¾Ð¼Ð¾Ð³Ð°ÐµÑ ÑÐ¸ÐºÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð°Ð²ÑÐ¾ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½Ð¾Ðµ Ð¿Ð¾Ð²ÐµÐ´ÐµÐ½Ð¸Ðµ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð² Ð² Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ðµ Ð¸ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·ÑÐµÑ ÑÐ´Ð²Ð¸Ð½ÑÑÑÐµ Ð²Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸ ÑÐµÐ¿Ð»Ð¸ÐºÐ¸ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð². ÐÑÐ½Ð¾Ð²Ð½Ð¾Ð¹ Ð°ÑÐ¿ÐµÐºÑ Ð¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ DPCA Ð²ÐºÐ»ÑÑÐ°ÐµÑ Ð²ÑÐ±Ð¾Ñ Ð·Ð°Ð´ÐµÑÐ¶ÐµÐº Ð¸ ÐºÐ¾Ð¼Ð¿Ð¾Ð½ÐµÐ½ÑÐ¾Ð² Ð´Ð»Ñ Ð¾ÐºÐ¾Ð½ÑÐ°ÑÐµÐ»ÑÐ½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸. ÐÐ¾Ð´ÐµÐ»Ñ ANN Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÐ¾Ð´Ð° DPCA Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° NSGA-II Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÐµÑ Ð½Ð°Ð¼ Ð½Ð°ÑÐ¾Ð´Ð¸ÑÑ Ð±Ð¾Ð»ÐµÐµ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾Ðµ ÑÐµÑÐµÐ½Ð¸Ðµ Ð´Ð»Ñ Ð¼Ð½Ð¾Ð³Ð¾ÐºÑÐ¸ÑÐµÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð¹ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ. ÐÐµÑÐ¾Ð´ Ð¿Ð¾Ð¼Ð¾Ð³Ð°ÐµÑ Ð¾Ð¿ÑÐ¸Ð¼Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð¿ÑÐ¾ÑÐµÑÑ Ð² Ð¿ÑÐ¾Ð¼ÑÑÐ»ÐµÐ½Ð½ÑÑ ÑÐ¸ÑÑÐµÐ¼Ð°Ñ Ð¸ ÑÐ¸ÑÑÐµÐ¼Ð°Ñ Ñ Ð±Ð¾Ð»ÑÑÐ¸Ð¼ ÐºÐ¾Ð»Ð¸ÑÐµÑÑÐ²Ð¾Ð¼ Ð¿Ð°ÑÐ°Ð¼ÐµÑÑÐ¾Ð².