scholarly journals Classification of asteroid families with artificial neural networks

2020 ◽  
pp. 39-48
Author(s):  
D. Vujicic ◽  
R. Pavlovic ◽  
D. Milosevic ◽  
B. Djordjevic ◽  
S. Randjic ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Neural Network Training ◽  
Network Training ◽  
Artificial Neural ◽  
Artificial Neural Network Training ◽  
Asteroid Families

This paper describes an artificial neural network for classification of asteroids into families. The data used for artificial neural network training and testing were obtained by the Hierarchical Clustering Method (HCM). We have shown that an artificial neural networks can be used as a validation method for the HCM on families with a large number of members.

A Study on the Image Classification Techniques Based on Wavelet Artificial Neural Network Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 3512-3514
Author(s):  
Xue Ding ◽  
Hong Hong Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Training Methods ◽  
Neural Network Training ◽  
Target Vector ◽  
Network Training ◽  
Incremental Training ◽  
Artificial Neural ◽  
Artificial Neural Network Training

With the ever-changing education information technology, it is a big problem for the universities and college that how to classify the thousands of copies of the image during the art examination marking process. This paper is to explore the application of artificial intelligence techniques, and to do accurate classification of a large number of images within a limited time and under the help of computer. It is can be seen that the proposed method is feasible through the application of the results of the actual work. Artificial neural network training Artificial neural network training methods have two mainly style, which are Incremental Training and Batch Training, and take the amount of different network training mission as the distinction standard. First, to introduce the Incremental Training [1], that means whenever the network receives the input vector and target vector, it have to adjust once the connection weights and thresholds. It is an online learning method. The other one is Batch Training [2], that means no longer adjust the connection and immediately, but perform bulk adjustment, and after a given volume of the input vector and target vector. Both training methods can be applied, whether it is static or dynamic neural network. Different results will be obtained by artificial neural network for the use of different training methods. When using artificial neural networks to solve specific problems, learning methods, training methods and artificial neural network function should be selected according to the expected results of question type and its specific requirements [3-4]. The selection of parameters of wavelet neural networks and adaptive learning

Multi-ReRAM Synapses for Artificial Neural Network Training

2019 ◽  
Author(s):  
Irem Boybat ◽  
Cecilia Giovinazzo ◽  
Elmira Shahrabi ◽  
Igor Krawczuk ◽  
Iason Giannopoulos ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Training ◽  
Network Training ◽  
Artificial Neural ◽  
Artificial Neural Network Training
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