Online tool wear prediction in drilling operations using selective artificial neural network ensemble model

2011 ◽  
Vol 20 (4) ◽  
pp. 473-485 ◽  
Author(s):  
Jianbo Yu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Tool Wear ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Wear Prediction ◽  
Online Tool ◽  
Tool Wear Prediction ◽  
Drilling Operations ◽  
Artificial Neural Network Ensemble

An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images

Energy ◽  
2013 ◽  
Vol 61 ◽  
pp. 636-645 ◽  
Author(s):  
Alvaro Linares-Rodriguez ◽  
José Antonio Ruiz-Arias ◽  
David Pozo-Vazquez ◽  
Joaquin Tovar-Pescador
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Satellite Images ◽  
Global Solar Radiation ◽  
Ensemble Model ◽  
Neural Network Ensemble ◽  
Artificial Neural ◽  
Artificial Neural Network Ensemble

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

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.

TOOL WEAR PREDICTION FROM ACOUSTIC EMISSION AND SURFACE CHARACTERISTICS VIA AN ARTIFICIAL NEURAL NETWORK

1999 ◽  
Vol 13 (6) ◽  
pp. 955-966 ◽  
Author(s):  
P. WILKINSON ◽  
R.L. REUBEN ◽  
J.D.C. JONES ◽  
J.S. BARTON ◽  
D.P. HAND ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Tool Wear ◽  
Surface Characteristics ◽  
Wear Prediction ◽  
Tool Wear Prediction ◽  
Artificial Neural
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