Applying Neural Networks to Air Force Personnel Analysis

Abstract

The principal objective of this task involved evaluating artificial neural networks for application to personnel modeling by examining areas representative of many personnel models. The four areas chosen were airmen re- enlistment, the determinants of reenlistment, and the effects of policy levers; pilot training and more specifically the likelihood of candidates successfully completing Undergraduate Pilot Training; projection of aggregate time series personnel flow rates; and productive capacity of airmen as it relates to aptitude and experience. In addition, the productive capacity analysis was expanded into a working computer prototype allowing the user to examine the effect of changing aptitude/experience mixes on productive capacity. Performance was compared against traditional techniques such as regression analysis. Artificial neural networks, Learning vector quantization, Probabilistic networks, Back propagation, Personnel system modeling.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1992
Accession Number
ADA248762

Entities

People

  • Larry T. Looper
  • Sheree K. Engquist
  • Vince L. Wiggins

Tags

Communities of Interest

  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Air Force Personnel
  • Artificial Intelligence
  • Computational Science
  • Data Mining
  • Data Science
  • Employment
  • Enlisted Personnel
  • Human Resources
  • Information Processing
  • Information Science
  • Information Systems
  • Knowledge Management
  • Management Personnel
  • Neural Networks
  • Personnel Management
  • Regression Analysis
  • Statistical Algorithms

Readers

  • Computational Modeling and Simulation
  • Naval Personnel Management
  • Research Science/Academic Research

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy
  • AI & ML - Neural Networks