Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
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.
How to cite this article:
Junli Yang , David J. Edwards and P.E.D. Love . Classifying Plant Operator Productivity Using Computational Science.
DOI: https://doi.org/10.36478/ajit.2004.336.346
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2004.336.346