Knowledge discovery and integration based on a novel neural network ensemble model

2006 ◽  
Author(s):  
Yong Wang Yong Wang ◽  
Hong-Jie Xing Hong-Jie Xing
Keyword(s):  
Neural Network ◽  
Knowledge Discovery ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Discovery And Integration

An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery

Energy ◽  
2015 ◽  
Vol 91 ◽  
pp. 264-273 ◽  
Author(s):  
Alvaro Linares-Rodriguez ◽  
Samuel Quesada-Ruiz ◽  
David Pozo-Vazquez ◽  
Joaquin Tovar-Pescador
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

Computer-aided diagnosis of cervical cytopathology based on neural network ensemble model

2018 ◽  
Vol 33 (4) ◽  
pp. 347-356
Author(s):  
廖 欣 LIAO Xin ◽  
郑 欣 ZHENG Xin ◽  
邹 娟 ZOU Juan ◽  
冯 敏 FENG Min ◽  
孙 亮 SUN Liang ◽  
...  
Keyword(s):  
Neural Network ◽  
Computer Aided Diagnosis ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A novel time difference of arrival localization algorithm using a neural network ensemble model

2018 ◽  
Vol 14 (11) ◽  
pp. 155014771881579 ◽  
Author(s):  
Zhenkai Zhang ◽  
Feng Jiang ◽  
Boyuan Li ◽  
Bing Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mobile Station ◽  
Time Difference ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Time Difference Of Arrival ◽  
Generalization Ability ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

scholarly journals A Novel Dynamic Weight Neural Network Ensemble Model

2015 ◽  
Vol 11 (8) ◽  
pp. 862056 ◽  
Author(s):  
Kewen Li ◽  
Wenying Liu ◽  
Kang Zhao ◽  
Mingwen Shao ◽  
Lu Liu
Keyword(s):  
Neural Network ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Dynamic Weight
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