Research on Improved IPSO-LSSVM Method and Its Application
Authors
Peng-fei LIU, Qun-tai SHEN, Jun ZHI
Corresponding Author
Peng-fei LIU
Available Online December 2016.
- DOI
- 10.2991/cnct-16.2017.89How to use a DOI?
- Keywords
- Particle Swarm Optimization, Support Vector Machine, Cost Prediction
- Abstract
It adopts support vector machine which is applicable for small sample prediction and constructs the prediction model. It is based on analyzing characteristics of particle swarm optimization and support vector machine. The improved IPSO-LSSVM prediction model shall be used to predict the development cost of military excavator. The prediction result indicates that compared with traditional SVM algorithm and BP algorithm, the prediction model has a better small sample adaptability, a faster training velocity and a higher prediction accuracy and it is more applicable to predict the development cost of engineering equipment.
- Copyright
- © 2017, 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 - Peng-fei LIU AU - Qun-tai SHEN AU - Jun ZHI PY - 2016/12 DA - 2016/12 TI - Research on Improved IPSO-LSSVM Method and Its Application BT - Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016) PB - Atlantis Press SP - 644 EP - 648 SN - 2352-538X UR - https://doi.org/10.2991/cnct-16.2017.89 DO - 10.2991/cnct-16.2017.89 ID - LIU2016/12 ER -