An evaluation of artificial neural network modeling for manpower analysis
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Authors
Byrne, Brian James.
Subjects
Neural networks
Modeling techniques
Voluntary separation programs
VSI
SSB
Marine Corps separations incentives
Modeling techniques
Voluntary separation programs
VSI
SSB
Marine Corps separations incentives
Advisors
Thomas, George W.
Hill, Timothy R.
Date of Issue
1993-09
Date
September 1993
Publisher
Monterey, California. Naval Postgraduate School
Language
en_US
Abstract
This thesis evaluates the capabilities of artificial neural networks in forecasting the take-rates of the Voluntary Separations Incentive/Special Separations Benefit (VSI/SSB) programs for male, Marine Corps Enlisted Personnel in the grades of E-5 and E-6. The Artificial Neural Networks models are compared with the forecasting abilities of a classical regression model. The data are taken from the Headquarters Marine Corps Enlisted Master File which contains military and personal background on each enlisted member of the United States Marine Corps. The classical regression model is a casual model constructed based upon the theory of occupational job choice. The neural network models are presented with all available data elements. Empirical results indicate that artificial neural networks provide forecasting results at least as good as, if not better than, those obtained using classical regression techniques. However, artificial neural networks are limited in their usefulness for policy analysis. Neural networks, Modeling techniques, Voluntary separation programs, VSI, SSB, Marine Corps separations incentives.
Type
Thesis
Description
Series/Report No
Department
Department of Administrative Sciences
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funding
Format
135 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.