Multi-mother Wavelet Neural Network Training Using Genetic Algorithm-Based Approach to Optimize and Improves the Robustness of Gradient-Descent Algorithms: 3D Mesh Deformation Application

2019 ◽  
pp. 98-108 ◽  
Author(s):  
Naziha Dhibi ◽  
Chokri Ben Amar
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Gradient Descent ◽  
Wavelet Neural Network ◽  
Mesh Deformation ◽  
Neural Network Training ◽  
Mother Wavelet ◽  
3D Mesh ◽  
Descent Algorithms ◽  
Network Training

scholarly journals Gradient Descent is a Technique for Learning to Learn

2018 ◽  
Vol 5 (2) ◽  
pp. 145-156 ◽  
Author(s):  
Taposh Kumar Neogy ◽  
Naresh Babu Bynagari
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Optimization Methods ◽  
Neural Network Training ◽  
Learning To Learn ◽  
Learning Problem ◽  
Convex Problems ◽  
Network Training ◽  
Simple Convex ◽  
Learned Features

In machine learning, the transition from hand-designed features to learned features has been a huge success. Regardless, optimization methods are still created by hand. In this study, we illustrate how an optimization method's design can be recast as a learning problem, allowing the algorithm to automatically learn to exploit structure in the problems of interest. On the tasks for which they are taught, our learning algorithms, implemented by LSTMs, beat generic, hand-designed competitors, and they also adapt well to other challenges with comparable structure. We show this on a variety of tasks, including simple convex problems, neural network training, and visual styling with neural art.  

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.

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