Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science

State Forecast Method of Electronic Equipments Based on Improved HMM and LS-SVM

Authors
Jianzhong Zhao, Wen Ye, Yong Liu, Tao Jiang
Corresponding Author
Jianzhong Zhao
Available Online July 2015.
DOI
https://doi.org/10.2991/lemcs-15.2015.30How to use a DOI?
Keywords
Parameter estimation; Hidden Markov model(HMM); Least square support vector machine(LS-SVM); Multi-agent genetic algorithm (MAGA); State forecast
Abstract
For the deficiency that the traditional single forecast methods could not forecast the states of electronic equipments, a combined forecast method based on hidden Markov model (HMM) and least square support vector machine (LS-SVM) is presented. Multi-agent genetic algorithm (MAGA) is used to estimate parameters of HMM for overcoming the problem that Baum-Welch algorithm is easy to fall into local optimal solution easily. MAGA is used to estimate parameters of LS-SVM. On the base of these, the combined forecast model of electronic equipment states is established. The example analysis results shows the superiority of the combined forecast model at forecast precision, calculation speed and stability.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
Part of series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-6252-102-5
ISSN
1951-6851
DOI
https://doi.org/10.2991/lemcs-15.2015.30How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Jianzhong Zhao
AU  - Wen Ye
AU  - Yong Liu
AU  - Tao Jiang
PY  - 2015/07
DA  - 2015/07
TI  - State Forecast Method of Electronic Equipments Based on Improved HMM and LS-SVM
BT  - International Conference on Logistics Engineering, Management and Computer Science (LEMCS 2015)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/lemcs-15.2015.30
DO  - https://doi.org/10.2991/lemcs-15.2015.30
ID  - Zhao2015/07
ER  -