A Principal Components Analysis Self-Organizing Neural Network Model and Computational Experiment

2013 ◽  
Vol 756-759 ◽  
pp. 3330-3335
Author(s):  
Ji Fu Nong
Keyword(s):  
Neural Network ◽  
Principal Components Analysis ◽  
Network Model ◽  
Neural Network Model ◽  
Principal Components ◽  
Computational Experiment ◽  
Neural Model ◽  
Self Organizing Map ◽  
Components Analysis ◽  
Self Organizing

We propose a new self-organizing neural model that performs principal components analysis. It is also related to the adaptive subspace self-organizing map (ASSOM) network, but its training equations are simpler. Experimental results are reported, which show that the new model has better performance than the ASSOM network.

An improved self-organizing incremental neural network model for short-term time-series load prediction

2021 ◽  
Vol 292 ◽  
pp. 116912
Author(s):  
Rong Wang Ng ◽  
Kasim Mumtaj Begam ◽  
Rajprasad Kumar Rajkumar ◽  
Yee Wan Wong ◽  
Lee Wai Chong
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Load Prediction ◽  
Short Term ◽  
Self Organizing

scholarly journals CONSTRUCTION OF NEURAL NETWORK MODEL FOR STUDYING OF CRYSTAL STRUCTURES PROPERTIES

2021 ◽  
Author(s):  
Olga Uvarova ◽  
Sergey Uvarov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Total Energy ◽  
Crystal Structures ◽  
Network Model ◽  
Neural Network Model ◽  
Cohesive Energy ◽  
Computational Experiment ◽  
Model Based ◽  
Artificial Neural

The paper considers a mechanism for constructing a model based on artificial neural network for obtaining the values of the cohesive energy of a system of atoms. Cohesive energy allows for calculation of total energy of system. It is one of the most important characteristics of a structure. A computational experiment is carried out for one-component crystal structures of Si, Ge and C.

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