Improved NN-GM(1,1) for Postgraduates’ Employment Confidence Index Forecasting
Author
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-14
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Postgraduates’ employment confidence index (ECI) forecasting can help the university to predict the future trend of postgraduates’ employment.
However, the common forecast method based on the grey model (GM) has unsatisfactory performance to a certain extent.
In order to forecast postgraduates’ ECI efficiently, this paper discusses a novel hybrid forecast model using limited raw samples.
Different from previous work, the residual modified GM(1,1) model is combined with the improved neural network (NN) in this work.
In particullar, the hybrid model reduces the residue of the standard GM(1,1) model as well as accelerating the convergence rate of the standard NN.
After numerical studies, the illustrative results are provided to demonstrate the forecast performance of the proposed model.
In addition, some strategies for improving the postgraduates’ employment confidence have been discussed.
American Psychological Association (APA)
Wang, Lu. 2014. Improved NN-GM(1,1) for Postgraduates’ Employment Confidence Index Forecasting. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-473714
Modern Language Association (MLA)
Wang, Lu. Improved NN-GM(1,1) for Postgraduates’ Employment Confidence Index Forecasting. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-473714
American Medical Association (AMA)
Wang, Lu. Improved NN-GM(1,1) for Postgraduates’ Employment Confidence Index Forecasting. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-473714
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-473714