TY  - JOUR
T1  - Classifying Plant Operator Productivity Using Computational Science
AU - , Junli Yang AU - , David J. Edwards AU - , P.E.D. Love 
JO  - Asian Journal of Information Technology
VL  - 3
IS  - 5
SP  - 336
EP  - 346
PY  - 2004
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2004.336.346
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2004.336.346
KW  - 
AB  - This paper presents a conceptual model with which to classify plant operator productivity using the artificial intelligent technique, neural networks (ANN). Specially, an artificial network model is proposed that uses factors such as: operator`s motivation, management role, maintenance task taken, stress and fatigue, education and training. Within these broad ‘generic` factors, a comprehensive range of variables exist. The ANN system design proposes a feed-forward multiplayer perceptron with back-propagation algorithm that will predict three levels of operator` productivity (namely high, medium and low). It is then proposed that the maths and algorithms developed be incorporated into a web-based software solution that connects databases of information, held on a server with dual connectivity capabilities, to users using Active Server Pages (ASP) programming code. Using this approach, it is anticipated that a user-friendly package will be developed that will enable the widest possible practitioner audience to access the software, anywhere on the planet, anytime of day.
ER  -