References
237
29. Kingma DP, Ba J Adam (2014) A method for stochastic optimization. https://arxiv.org/abs/
1412.6980
30. Zhang J, Zhao D, Gao W (2014) Group-based sparse representation for image restoration.
IEEE Trans Image Process 23(8):3336–3351
31. Ren D, Zhang H, Zhang D et al (2015) Fast total-variation based image restoration based on
derivative alternated direction optimization methods. Neurocomputing 170:201–212
32. Abdel-Zaher AM, Eldeib AM (2016) Breast cancer classification using deep belief networks.
Exp Syst Appl 46:139–144
33. Fu G (2018) Deep belief network based ensemble approach for cooling load forecasting of
air-conditioning system. Energy 148:269–282
34. Kuremoto T, Kimura S, Kobayashi K et al (2014) Time series forecasting using a deep belief
network with restricted Boltzmann machines. Neurocomputing 137:47–56
35. Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and
prediction, methodology, data representations, and case studies. Exp Syst Appl 83:187–205
36. Gaukler G, Ketzenberg M, Salin V (2017) Establishing dynamic expiration dates for
perishables: An application of rfid and sensor technology. Int J Prod Econ 193:617–632
37. Chen JC, Cheng CH, Huang PTB et al (2013) Warehouse management with lean and RFID
application: a case study. Int J Adv Manuf Technol 69(1–4):531–542
38. Yu Y, Yu X, Zhao Z et al (2016) Measurement uncertainty limit analysis of biased estimators
in RFID multiple tags system. IET Sci Meas Technol 10(5):449–455
39. Yadav AK, Schandel S (2014) Solar radiation prediction using Artificial Neural Network
techniques: a review. Renew Sustain Energy Rev 33:772–781
237
29. Kingma DP, Ba J Adam (2014) A method for stochastic optimization. https://arxiv.org/abs/
1412.6980
30. Zhang J, Zhao D, Gao W (2014) Group-based sparse representation for image restoration.
IEEE Trans Image Process 23(8):3336–3351
31. Ren D, Zhang H, Zhang D et al (2015) Fast total-variation based image restoration based on
derivative alternated direction optimization methods. Neurocomputing 170:201–212
32. Abdel-Zaher AM, Eldeib AM (2016) Breast cancer classification using deep belief networks.
Exp Syst Appl 46:139–144
33. Fu G (2018) Deep belief network based ensemble approach for cooling load forecasting of
air-conditioning system. Energy 148:269–282
34. Kuremoto T, Kimura S, Kobayashi K et al (2014) Time series forecasting using a deep belief
network with restricted Boltzmann machines. Neurocomputing 137:47–56
35. Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and
prediction, methodology, data representations, and case studies. Exp Syst Appl 83:187–205
36. Gaukler G, Ketzenberg M, Salin V (2017) Establishing dynamic expiration dates for
perishables: An application of rfid and sensor technology. Int J Prod Econ 193:617–632
37. Chen JC, Cheng CH, Huang PTB et al (2013) Warehouse management with lean and RFID
application: a case study. Int J Adv Manuf Technol 69(1–4):531–542
38. Yu Y, Yu X, Zhao Z et al (2016) Measurement uncertainty limit analysis of biased estimators
in RFID multiple tags system. IET Sci Meas Technol 10(5):449–455
39. Yadav AK, Schandel S (2014) Solar radiation prediction using Artificial Neural Network
techniques: a review. Renew Sustain Energy Rev 33:772–781
Précédent
