TY  - JOUR
AU  - Singh, Min-jun
AU  - Haddad, Thabo
AU  - Nasser, Hiroshi
AU  - Rahman, Kofi
TI  - Artificial Neural Networks for Convolutional Neural Networks
JO  - Open Access Journal of Engineering Applications
PY  - 2018
VL  - 42
IS  - 2
SP  - 413
EP  - 438
AB  - Industry increasingly demands predictive tools that link convolutional neural networks to the observable performance of supply chain logistics. A comprehensive framework based on artificial neural networks is developed and applied to representative cases drawn from supply chain logistics. Predicted values agreed with the experimental data to within 21%, confirming the model. The approach is particularly well suited to settings where data and resources are limited.
KW  - convolutional neural networks
KW  - artificial neural networks
KW  - corrosion
KW  - additive manufacturing
KW  - electric vehicle
KW  - model predictive control
SN  - 3105-7004
DO  - 10.55555/oajea.2018.4202.023
UR  - https://journal.bsaps.com/article/9540
ER  -