Hybrid system with genetic algorithm and artificial neural networks and its application to retail credit risk assessment

2012 ◽  
Vol 39 (16) ◽  
pp. 12605-12617 ◽  
Author(s):  
Stjepan Oreski ◽  
Dijana Oreski ◽  
Goran Oreski
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Risk Assessment ◽  
Artificial Neural Networks ◽  
Credit Risk ◽  
Hybrid System ◽  
Credit Risk Assessment ◽  
Retail Credit ◽  
Artificial Neural

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

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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