Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy consumption

2006 ◽  
Author(s):  
A. Azadeh ◽  
S. F. Ghaderi ◽  
S. Tarverdian ◽  
M. Saberi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Electrical Energy Consumption ◽  
Artificial Neural

Modelling of electrical energy consumption in an electric arc furnace using artificial neural networks

Energy ◽  
2016 ◽  
Vol 108 ◽  
pp. 132-139 ◽  
Author(s):  
Dragoljub Gajic ◽  
Ivana Savic-Gajic ◽  
Ivan Savic ◽  
Olga Georgieva ◽  
Stefano Di Gennaro
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Electrical Energy ◽  
Electric Arc Furnace ◽  
Electric Arc ◽  
Arc Furnace ◽  
Electrical Energy Consumption ◽  
Artificial Neural

MATHEMATICAL SUPPORT FOR INDUCTION SOLDERING CONTROL BASED ON INTELLIGENT METHODS OF INFORMATION PROCESSING

2021 ◽  
pp. 81-87
Author(s):  
А.В. Милов
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Information Processing ◽  
Mathematical Models ◽  
Technological Process ◽  
Artificial Neural ◽  
Intelligent Methods

В статье представлены математические модели на основе искусственных нейронных сетей, используемые для управления индукционной пайкой. Обучение искусственных нейронных сетей производилось с использованием многокритериального генетического алгоритма FFGA. This article presents mathematical models based on artificial neural networks used to control induction soldering. The artificial neural networks were trained using the FFGA multicriteria genetic algorithm. The developed models allow to control induction soldering under conditions of incomplete or unreliable information, as well as under conditions of complete absence of information about the technological process.

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