Optimization of Wind Turbine Pitch Controller by Neural Network Model Based on Latin Hypercube

2012 ◽  
Vol 36 (9) ◽  
pp. 1065-1071 ◽  
Author(s):  
Kwangk-Ki Lee ◽  
Seung-Ho Han
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Network Model ◽  
Neural Network Model ◽  
Latin Hypercube ◽  
Model Based

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.

scholarly journals Exploiting the Relationship between Pruning Ratio and Compression Effect for Neural Network Model Based on TensorFlow

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Bo Liu ◽  
Qilin Wu ◽  
Yiwen Zhang ◽  
Qian Cao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Computing Time ◽  
Experimental Results ◽  
Data Set ◽  
Compression Effect ◽  
Model Based ◽  
The Relationship

Pruning is a method of compressing the size of a neural network model, which affects the accuracy and computing time when the model makes a prediction. In this paper, the hypothesis that the pruning proportion is positively correlated with the compression scale of the model but not with the prediction accuracy and calculation time is put forward. For testing the hypothesis, a group of experiments are designed, and MNIST is used as the data set to train a neural network model based on TensorFlow. Based on this model, pruning experiments are carried out to investigate the relationship between pruning proportion and compression effect. For comparison, six different pruning proportions are set, and the experimental results confirm the above hypothesis.

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