Comparative Study of BP Neural Network and RBF Neural Network in Surface Reconstruction

2019 ◽  
Author(s):  
Hai-jun Wang ◽  
Tao Jin
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Surface Reconstruction ◽  
Bp Neural Network ◽  
Rbf Neural Network

Application of BP Neural Network into the Kow of Chemical Contaminants

2010 ◽  
Vol 121-122 ◽  
pp. 574-578
Author(s):  
Hui Yu Jiang ◽  
Min Dong ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Partition Coefficient ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Rbf Neural Network ◽  
Physical Parameters ◽  
Bp Network ◽  
Chemical Contaminants ◽  
Water Partition Coefficient ◽  
Molecular Bond

The octanol / water partition coefficient (Kow) is an important physical parameters to describe their behavior in the environment. However, because of some reasons, it is difficult to determine the octanol / water partition coefficient of each compound accurately. In this paper, we will introduce RBF neural network and molecular bond connectivity index to forecast the solubility of organic compounds in water. The result is better using the BP network to predict, the correlation coefficient has achieved 0.998, the prediction error in the permission scope.

A RBF Neural Network Approach for Fitting Creep Curve of Sandstone

2010 ◽  
Vol 171-172 ◽  
pp. 274-277
Author(s):  
Yun Liang Tan ◽  
Ze Zhang
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Creep Curve ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Reference Value ◽  
Network Approach ◽  
Neural Network Approach ◽  
Training Time ◽  
Set Up

In order to quest an effective approach for predicate the rheologic deformation of sandstone based on some experimental data, an improved approaching model of RBF neural network was set up. The results show, the training time of improved RBF neural network is only about 10 percent of that of the BP neural network; the improved RBF neural network has a high predicating accuracy, the average relative predication error is only 7.9%. It has a reference value for the similar rock mechanics problem.

A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification

2016 ◽  
Vol 29 (1) ◽  
pp. 245-256 ◽  
Author(s):  
Shikha Agrawal ◽  
Jitendra Agrawal ◽  
Shilpy Kaur ◽  
Sanjeev Sharma
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Rbf Neural Network
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