Research on Improved IPSO-LSSVM Method and Its Application

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/).

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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  -