A two-stage algorithm integrating genetic algorithm and modified Newton method for neural network training in engineering systems

2011 ◽  
Vol 38 (10) ◽  
pp. 12189-12194 ◽  
Author(s):  
Ching-Long Su ◽  
S.M. Yang ◽  
W.L. Huang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Newton Method ◽  
Engineering Systems ◽  
Neural Network Training ◽  
Two Stage ◽  
Network Training ◽  
Modified Newton Method

Soil Moisture Retrieval Based on ASAR Data and Genetic Neural Networks

2012 ◽  
Vol 500 ◽  
pp. 198-203
Author(s):  
Chang Lin Xiao ◽  
Yan Chen ◽  
Lina Liu ◽  
Ling Tong ◽  
Ming Quan Jia
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Soil Moisture ◽  
Training Data ◽  
Neural Network Training ◽  
Practical Test ◽  
Network Training

Genetic Algorithm can further optimize Neural Networks, and this optimized Algorithm has been used in many fields and made better results, but currently, it have not been used in inversion parameters. This paper used backscattering coefficients from ASAR, AIEM model to calculate data as neural network training data and through Genetic Algorithm Neural Networks to retrieve soil moisture. Finally compared with practical test and shows the validity and superiority of the Genetic Algorithm Neural Networks.

scholarly journals A two-stage subspace trust region approach for deep neural network training

2017 ◽  
Author(s):  
Viacheslav Dudar ◽  
Giovanni Chierchia ◽  
Emilie Chouzenoux ◽  
Jean-Christophe Pesquet ◽  
Vladimir Semenov
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Trust Region ◽  
Neural Network Training ◽  
Two Stage ◽  
Network Training ◽  
Region Approach
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