scholarly journals AERODYNAMIC DESIGN OPTIMIZATION OF UAV ROTOR BLADES USING A GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORKS

2014 ◽  
Vol 19 (3) ◽  
pp. 29-36
Author(s):  
H.M. Lee ◽  
J.K. Ryu ◽  
S.J. Ahn ◽  
O.J. Kwon
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Rotor Blades ◽  
Aerodynamic Design ◽  
Aerodynamic Design Optimization ◽  
Artificial Neural

Improved Multidisciplinary Design Optimization Based on Genetic Algorithm and Artificial Neural Networks and Its Application

2016 ◽  
Vol 13 (10) ◽  
pp. 6501-6508
Author(s):  
Yi Su ◽  
Fa-Yin Wang ◽  
Jian-Yu Zhao
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Computational Cost ◽  
System Level ◽  
Multidisciplinary Design ◽  
Local Optimum ◽  
Artificial Neural

Multidisciplinary Design Optimization (MDO) is an algorithm widely used in the engineering field currently. However, traditional MDO often leads to the failure of convergence or local optimum problems caused by convergence. In such cases, a multidisciplinary design optimization based on genetic algorithm (GA) and artificial neural networks (ANN) (GA-ANN-MDO) is presented in the paper. Under the thought of parallel distribution of traditional MDO, the real sub-disciplinary model is replaced by a highly precise ANN model dependent on the Latin Hypercube experimental design method in the GA-ANN-MDO, so as to reduce the computational cost and smooth the value noise. The GA optimization system level is applied to decline the possibility of partial solution involved in the optimization. As shown from the optimization results of two classic mathematical examples, GA-ANN-MDO is presented good robustness, which could quickly and effectively converge to the global optimal solution. In addition, a project example was employed finally to verify the feasibility of GA-ANN-MDO in the engineering.

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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