Genetic algorithm optimization of magnetic properties of Fe-Co-Ni nanostructure alloys prepared by the mechanical alloying by using multi-objective artificial neural networks for the core of transformer

2021 ◽  
pp. 102653
Author(s):  
Malihe Zeraati ◽  
Razieh Arshadizadeh ◽  
Narendra Pal Singh Chauhan ◽  
Ghasem Sargazi
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Magnetic Properties ◽  
Artificial Neural Networks ◽  
Mechanical Alloying ◽  
Algorithm Optimization ◽  
Genetic Algorithm Optimization ◽  
Multi Objective ◽  
The Core ◽  
Artificial Neural

Based on Artificial Neural Network Simulation of Alloy Finite Element

2013 ◽  
Vol 710 ◽  
pp. 739-742
Author(s):  
Shu Zhang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fem Analysis ◽  
Algorithm Optimization ◽  
Neural Network Simulation ◽  
Artificial Neurons ◽  
Artificial Neural

Artificial neural networks are composed of interconnecting artificial neurons (programming constructs that mimic the properties of biological neurons). Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. First modal analyses of microstructure defects are performed in ANSYS. Second the genetic algorithm is implemented in MATLAB to Calculate the Value of b and p. The last, The FEM analysis results are imported in ANSYS about the Stress distribution. The result presented in this paper is obtained using the Genetic Algorithm Optimization Toolbox.

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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