scholarly journals Remote Sensing Estimation of Chlorophyll-A in Case-II Waters of Coastal Areas: Three-Band Model Versus Genetic Algorithm–Artificial Neural Networks Model

2021 ◽  
Vol 14 ◽  
pp. 3640-3658
Author(s):  
Jinyue Chen ◽  
Shuisen Chen ◽  
Rao Fu ◽  
Chongyang Wang ◽  
Dan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chlorophyll A ◽  
Coastal Areas ◽  
Band Model ◽  
Case Ii Waters ◽  
Artificial Neural ◽  
Model Versus

Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks

2019 ◽  
Author(s):  
Lucas Silveira Kupssinsku ◽  
Taina Thomassim Guimaraes ◽  
Rafael de Freitas ◽  
Eniuce Menezes de Souza ◽  
Pedro Rossa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chlorophyll A ◽  
Suspended Solids ◽  
Artificial Neural

Determination of application volume for coffee plantations using artificial neural networks and remote sensing

2021 ◽  
Vol 184 ◽  
pp. 106096
Author(s):  
Mailson Freire de Oliveira ◽  
Adão Felipe dos Santos ◽  
Elizabeth Haruna Kazama ◽  
Glauco de Souza Rolim ◽  
Rouverson Pereira da Silva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coffee Plantations ◽  
Artificial Neural ◽  
Application Volume

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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