Nur Syahidah, Yusoff and Shamshuritawati, Sharif (2016) Identifying Influential Variables in Complex System: Network Topology Versus Principal Component Analysis. In: AIP Conference Proceedings: Advances in Industrial and Applied Mathematics Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences (SKSM23) , 24–26 November 2015 , Johor Bahru, Malaysia. pp. 1-6., 1750 (060023). ISBN 978-0-7354-1407-5
|
PDF
Identifying Influential Variables in Complex System- Network Topology Versus Principal Component Analysis.pdf Download (37kB) | Preview |
Abstract
High dimensional covariance structure can be considered as a complex system that relates each variable to the others in terms of variability. In complex system, identifying influential variables is a very important part of reliability analysis, which has been a key issue in analysing the structural organization of a system. To analyse such complex system, network topology and principal component analysis are constructed to simplify the system. Network topology can be used to simplify the information about the system and centrality measure will be used to interpret the network. In the other hand, the principal component analysis can be used to eliminate the variables that contribute little extra information. An example will be discussed to illustrate the advantage and disadvantage of network topology and principal component analysis and a recommendation will be presented.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Uncontrolled Keywords: | Complex systems; Network topology |
| Subjects: | Q Science > Q Science (General) |
| Faculty/Division: | Faculty of Industrial Sciences And Technology |
| Depositing User: | Mrs. Neng Sury Sulaiman |
| Date Deposited: | 18 May 2015 04:52 |
| Last Modified: | 29 Mar 2017 01:24 |
| URI: | http://umpir.ump.edu.my/id/eprint/9062 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |