Design Optimization Process Using Artificial Neural Networks, Bayesian Learning and Hybrid Algorithm

2008 ◽  
Author(s):  
Nhu-Van Nguyen ◽  
Kwon-Su Jeon ◽  
Jae-Woo Lee ◽  
Yung-Hwan Byun
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Hybrid Algorithm ◽  
Bayesian Learning ◽  
Optimization Process ◽  
Artificial Neural

Design optimization of electromagnetic devices using artificial neural networks

1992 ◽  
Vol 28 (5) ◽  
pp. 2805-2807 ◽  
Author(s):  
O.A. Mohammed ◽  
D.C. Park ◽  
F.G. Uler ◽  
C. Ziqiang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Optimization ◽  
Electromagnetic Devices ◽  
Artificial Neural

scholarly journals Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Ruijing Gan ◽  
Xiaojun Chen ◽  
Yu Yan ◽  
Daizheng Huang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hepatitis B ◽  
Hybrid Algorithm ◽  
Back Propagation ◽  
Grey Model ◽  
Early Prevention ◽  
Evaluation Indexes ◽  
Syndrome Surveillance ◽  
Artificial Neural

Accurate incidence forecasting of infectious disease provides potentially valuable insights in its own right. It is critical for early prevention and may contribute to health services management and syndrome surveillance. This study investigates the use of a hybrid algorithm combining grey model (GM) and back propagation artificial neural networks (BP-ANN) to forecast hepatitis B in China based on the yearly numbers of hepatitis B and to evaluate the method’s feasibility. The results showed that the proposal method has advantages over GM (1, 1) and GM (2, 1) in all the evaluation indexes.

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