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.

Prediction of Internal Stability for Geogrid-Reinforced Segmental Walls

2010 ◽  
Vol 163-167 ◽  
pp. 1854-1857
Author(s):  
Anuar Kasa ◽  
Zamri Chik ◽  
Taha Mohd Raihan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Statistical Methods ◽  
Retaining Wall ◽  
Retaining Walls ◽  
Internal Stability ◽  
Residual Soil ◽  
Concrete Masonry ◽  
Artificial Neural

Prediction of internal stability for segmental retaining walls reinforced with geogrid and backfilled with residual soil was carried out using statistical methods and artificial neural networks (ANN). Prediction was based on data obtained from 234 segmental retaining wall designs using procedures developed by the National Concrete Masonry Association (NCMA). The study showed that prediction made using ANN was generally more accurate to the target compared with statistical methods using mathematical models of linear, pure quadratic, full quadratic and interactions.

Estimating Penman–Monteith Reference Evapotranspiration Using Artificial Neural Networks and Genetic Algorithm: A Case Study

2012 ◽  
Vol 37 (4) ◽  
pp. 935-944 ◽  
Author(s):  
Seyed Saeid Eslamian ◽  
Seyed Alireza Gohari ◽  
Mohammad Javad Zareian ◽  
Alireza Firoozfar
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reference Evapotranspiration ◽  
Artificial Neural
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