Comparative analysis of BP neural network and RBF neural network in seismic performance evaluation of pier columns

2020 ◽  
Vol 141 ◽  
pp. 106707 ◽  
Author(s):  
Qinglin Liu ◽  
Panxu Sun ◽  
Xueyi Fu ◽  
Jian Zhang ◽  
Hong Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Comparative Analysis ◽  
Seismic Performance ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Seismic Performance Evaluation

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.

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