Advanced Search

Query:

Fill in the form and click »Search«...

Format:

Display: entries per page entries

Zbl 1187.90113
Pai, Ping-Feng
System reliability forecasting by support vector machines with genetic algorithms. (English)
[J] Math. Comput. Modelling 43, No. 3-4, 262-274 (2006). ISSN 0895-7177

Summary: Support vector machines (SVMs) have been used successfully to deal with nonlinear regression and time series problems. However, SVMs have rarely been applied to forecasting reliability. This investigation elucidates the feasibility of SVMs to forecast reliability. In addition, genetic algorithms (GAs) are applied to select the parameters of an SVM model. Numerical examples taken from the previous literature are used to demonstrate the performance of reliability forecasting. The experimental results reveal that the SVM model with genetic algorithms (SVMG) results in better predictions than the other methods. Hence, the proposed model is a proper alternative for forecasting system reliability.

MSC 2000:: *90B25 Reliability, etc.
90C59 Approximation methods and heuristics
68T05 Learning and adaptive systems

Keywords: support vector machines; genetic algorithms; system reliability; forecasting

Comment on this Item PDF MathML XML ASCII DVI PS BibTeX Online Ordering Article Journal

	Scientific prize winners of the ICM 2010
	Overhang
	Lie groups, physics and geometry. An introduction for physicists, engineers and chemists.

Advanced Search

Zentralblatt MATH Berlin [Germany]