Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites
Ping-Feng Pai 0, Wei-Chiang Hong, Feng-Min Lai, Jia-Hroung Wu, Shun-Lin Yang
0Department of Information Management, National Chi Nan Unive
Available Online October 2006.
- https://doi.org/10.2991/jcis.2006.86How to use a DOI?
- Support vector regression; immune algorithms; fatigue life prediction; composite materials
- Predicting fatigue life of composite materials is essential to increase reliability of manufacturing systems. The predicting techniques for fatigue life of composite materials are not widely investigated. The support vector regression (SVR) is an emerging forecasting technique and has been applied in many areas successfully. Therefore, this study attempts to examine the feasibility of SVR in predicting the fatigue life of composite materials. Additionally, immune algorithms (IA) are used to select three parameters of SVR models. An experimental data set from a laboratory was employed to depict the feasibility of develpoed SVRIA (support vector regression with immune algorithms) approach in predicting fatigue life of composite materials. Empirical results indicate that the SVRIA is a valid way in predicting fatigue life of composite materials.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Ping-Feng Pai AU - Wei-Chiang Hong AU - Feng-Min Lai AU - Jia-Hroung Wu AU - Shun-Lin Yang PY - 2006/10 DA - 2006/10 TI - Feasibility Assessment of Support Vector Regression Models with Immune Algorithms in Predicting Fatigue Life of Composites PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/jcis.2006.86 DO - https://doi.org/10.2991/jcis.2006.86 ID - Pai2006/10 ER -