Proceedings of the 4th Workshop on Advanced Research and Technology in Industry (WARTIA 2018)

LS-SVM Assessment of Weapon System Development Risk Based on SPA

Authors
Chunlan Wang, Xusheng Gan, Shenghou Li
Corresponding Author
Chunlan Wang
Available Online September 2018.
DOI
10.2991/wartia-18.2018.48How to use a DOI?
Keywords
Set pair analysis; LS-SVM; Weapon system development; Risk assessment
Abstract

To effectively prevent and development risk of weapon weapon system, a comprehensive assessment method based on Set Pair Analysis (SPA) theory and Least Square Support Vector Machine (LS-SVM) method is proposed for weapon weapon system development. firstly, on the basis of built weapon weapon system development risk assessment index system, the concept of connection degree and set pair in SPA theory is introduced to construct the training samples and test samples.Then through the obtained samples LS-SVM is trained and tested to get the assessment model and give the assessment result. The example shows that, the proposed method has many advantages in simple implement, combining qualitative and quantitative analysis, and easy understanding.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 4th Workshop on Advanced Research and Technology in Industry (WARTIA 2018)
Series
Advances in Engineering Research
Publication Date
September 2018
ISBN
978-94-6252-597-9
ISSN
2352-5401
DOI
10.2991/wartia-18.2018.48How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Chunlan Wang
AU  - Xusheng Gan
AU  - Shenghou Li
PY  - 2018/09
DA  - 2018/09
TI  - LS-SVM Assessment of Weapon System Development Risk Based on SPA
BT  - Proceedings of the 4th Workshop on Advanced Research and Technology in Industry (WARTIA 2018)
PB  - Atlantis Press
SP  - 271
EP  - 274
SN  - 2352-5401
UR  - https://doi.org/10.2991/wartia-18.2018.48
DO  - 10.2991/wartia-18.2018.48
ID  - Wang2018/09
ER  -