 Performance improvement of software-based system using an integrated approach – a case studyR. Amuthakkannan International Journal of Information Systems and Change Management, 2008, vol. 3, issue 4, 327-343 Abstract: The modern automation system consists of software and hardware components to achieve the high quality products and processes. In such type of software-based systems, optimal design is more important to improve the system performance. The perfect parameter design problems are complex because of non-linear relationships and interactions may occur among parameters. So, a proper approach is needed for a parameter optimal design. An integrated approach of neural network with genetic algorithms is proposed to address the optimal design of software-based automation system. This article outlines neural network methodology to predict the response of the software-based automation system for various process parameters values. Then, the genetic algorithm is used to predict the quantitative value of process parameter to improve the performance of the system. In this work, a cascading electro-pneumatic kit is taken as case analysis to analyse the performance of software-based system. Keywords: genetic algorithms; neural networks; parameter optimisation; process change; software-based automation; parameter design; optimal design; process parameters; cascading electropneumatics. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijiscm:v:3:y:2008:i:4:p:327-343 Access Statistics for this article More articles in International Journal of Information Systems and Change Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.