A non-parametric learning algorithm for small manufacturing data sets
Expert Systems with Applications: An International Journal
Editorial: Neural networks: Algorithms and applications
Neurocomputing
Thermodynamic analysis of variable speed refrigeration system using artificial neural networks
Expert Systems with Applications: An International Journal
Estimation of a data-collection maturity model to detect manufacturing change
Expert Systems with Applications: An International Journal
A new approach for manufacturing forecast problems with insufficient data: the case of TFT---LCDs
Journal of Intelligent Manufacturing
Hi-index | 12.05 |
Environment characteristics are dynamic and changeable. In customized or flexible manufacturing systems, the collected data used for analysis is often small. There are many studies on small data set problems. However, most papers attack the problem by developing data pre-treatment methods which normally require abstruse mathematical knowledge, deterring engineers from applying the methods in practice. This paper develops a unique neural network to accurately predict small data sets. This neural network is developed based on the concept of the data central location tracking method (CLTM) to determine net weights as the learning rules. It not only makes accurate forecasts using small data sets but it also facilitates knowledge learning for engineers.