Title :
Forecasting Equipment Readiness Based on SVM
Author :
XiangBo, Zhang ; Guojian, Mei ; Zongchang, Xu
Author_Institution :
Dept. of Tech. Support, Armored Force Eng. Inst., Beijing
Abstract :
In the paper, SVM (support vector machines) with SRM is aided to forecast readiness and sustainable capability, which can be improved by machine learning. The status parameters of armored vehicle engine are used as a case to analyses, establishes a model to forecast, which can be optimized in model indexes. Finally, the conclusion comes to the validity of method.
Keywords :
learning (artificial intelligence); support vector machines; traffic engineering computing; SVM; armored vehicle engine; forecasting equipment; machine learning; support vector machines; sustainable capability; Arithmetic; Artificial intelligence; Automotive engineering; Competitive intelligence; Engines; Machine learning; Predictive models; Space technology; Support vector machines; Vehicles; Equipment Readiness; Forecast Model; Model Validity; SVM;
Conference_Titel :
Computer Science and Software Engineering, 2008 International Conference on
Conference_Location :
Wuhan, Hubei
Print_ISBN :
978-0-7695-3336-0
DOI :
10.1109/CSSE.2008.1298
