A PCA-BPN approach for estimating simulation workload in cloud manufacturing

Author

Toly Chen

Author_Institution

Dept. of Ind. Eng. & Syst. Manage., Feng Chia Univ., Taichung, Taiwan

fYear

2015

fDate

7-10 July 2015

Firstpage

826

Lastpage

830

Abstract

Cloud manufacturing is a novel manufacturing technology that supports factories distributed around the world with ubiquitous accesses to manufacturing resources. Estimating the simulation workload for simulating a factory online is an important topic to cloud manufacturing. To investigate this, a principal component analysis (PCA)-back propagation network (BPN) approach is proposed in this study. The real data of some simulation tasks have been collected to test the proposed methodology. The experimental results supported the superiority of the proposed methodology over some existing methods in terms of the estimation accuracy.

Keywords

backpropagation; cloud computing; computer aided manufacturing; principal component analysis; ubiquitous computing; PCA-BPN approach; cloud manufacturing; manufacturing technology; principal component analysis-back propagation network; simulation workload estimation; ubiquitous manufacturing resource access; Backpropagation; Computational modeling; Estimation; Genetic programming; Testing; Training; Virtualization; Cloud manufacturing; back propagation network; estimation; principal component analysis; simulation workload; ubiquitous;

fLanguage

English

Publisher

ieee

Conference_Titel

Ubiquitous and Future Networks (ICUFN), 2015 Seventh International Conference on

Conference_Location

Sapporo

ISSN

2288-0712

Type

conf

DOI

10.1109/ICUFN.2015.7182658

Filename

7182658