Architecture and Models for System-Level Computer-Aided Design of the Management System of Energy Efficiency of Technological Processes at the Enterprise-Reference-Cited by-同舟云学术

Architecture and Models for System-Level Computer-Aided Design of the Management System of Energy Efficiency of Technological Processes at the Enterprise

Published:2017-11-22 Issue: Volume: Page:538-557
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing II
language:
Short-container-title:

Author:

Teslyuk Taras,Tsmots Ivan,Teslyuk Vasyl,Medykovskyy Mykola,Opotyak Yuriy

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-70581-1_38

Reference19 articles.

1. Medykovskyi, M.O., Tsmots, I.G., Tsymbal, Y.V.: Information analytical system for energy efficiency management at enterprises in the city of Lviv (Ukraine). Actual Probl. Ekonomics 1(175), 379–384 (2016)

2. Medykovskyi, M.O., Tsmots, I.G., Tsymbal, Y.V.: Intelligent data processing tools in the systems of energy efficiency management for regional economy. Actual Probl. Ekonomics 12(150), 271–277 (2013)

3. Apaкeлoв, B.E., Кpeмep, A.И.: Meтoдичecкиe вoпpocы экoнoмии энepгopecypcoв. M: Энepгoaтoмиздaт. 188 c (1990)

4. Гaнжa, B.Л.: Ocнoвы эффeктивнoгo иcпoльзoвaния энepгopecypcoв. Mн.: Бeлopycкaя нayкa. 452 c (2007)

5. Mинaeв, И.Г., Caмoйлeнкo, B.B.: Пpoгpaммиpyeмыe лoгичecкиe кoнтpoллepы: пpaктичecкoe pyкoвoдcтвo для нaчинaющeгo инжeнepa. Cтaвpoпoль: AГPУC, 100 c (2009)

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Neuro-controller implementation for the embedded control system for mini-greenhouse;PeerJ Computer Science;2023-11-13

2. The correlates of energy management practices and sales performance of small family food firms in Turkey;British Food Journal;2021-12-22

3. Capabilities and Application of Bithreshold Neurons and Networks;Advances in Intelligent Systems and Computing;2020-12-23

4. Synthesis of the Centered Bithreshold Neural Network Classifier;Advances in Intelligent Systems and Computing;2020-12-23

5. Representational Capabilities and Learning of Bithreshold Neural Networks;Advances in Intelligent Systems and Computing;2020-07-26