Abstract
It is important for us to evaluate each trainee’s ability with the aim of improving BDAR training efficiency. The typical assessment methods include fuzzy evaluation, gray correlation evaluation, neural network and so on. All of these methods are not able to make full use of the historical information. Determining membership function in first two methods is not easy. And ANN needs a lot of data sample which is difficult to obtain in BDAR training. So we can’t use these methods to model the assessment of personnel BDAR ability. Then we introduce Bayesian Stochastic Assessment Method which can deal well with the nonlinear and random problem. Each indexes’ standard is given according to the characteristics of BDAR training. A modified normal distribution which can make full use of historical information was put forward to determine the prior probability. And the poster probability is determined by distance method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cheeseman P, Kelly J, Self M, Stutz J, Taylor W, Freeman D (1988) Auto class: A Bayesian classification system. In: Proceedings of the 15th International conference on machine learning, vol 140, pp 52–65
Cheng P, Chen XR, ChenGJ, Wu CY (1985) Parameters estimation (Chinese) Shanghai science technology princess, Pudong, pp 20–100
Fishman, Monte Carlo (1996): Concepts, algorithms, and applications, Springer, New York, pp 85–122
Fried N, Geiger D, Goldszmidt M (1997) Bayesian network classifiers. Mach Learn 2–3(29):131–163
Gu XP, Ai JL, Han H (2011) Direction-determination ability evaluation based on interval number grey relational analysis(Chinese). Aerosp Electron Warf 27(3):26–29
Hyacinth SN (1999) Intelligent tutoring systems: an overview. Artif Intell Rev 40(4):251–277
Jia H (1995) A modified arithmetic determining weight in AHP (Chinese). WTUSM Bulletin of, Sci Technol, pp 25–30
** KR, Sun CH (1996) Chaotic recurrent neural networks and their application to speech recognition. Neuron Computing 13(224):281–294
Li JP, Shi Q, Gan MZ (2000) BDAR theory and application (Chinese), Army industry publitions, pp 10–50
Li M, Sun SY, Lv GZ (2003) Design and implementation of evaluation model of simulative maintenance training (Chinese). Comput Eng 29(9):186–188
Liang C (2011) Evaluating quality model of concrete construction project using Bayes disciminant analysis method (Chinese) Concr 259:50–53
Park KS, Kim SH (1999) Tools for interactive multi-attribute decision: Range-based approach. Eur J Oper Res 118:139–152
Patz Richard J, Junker Brian W (1999) A Straightforward approach to Markov chain Monte Carlo methods for item response models. J Educational Behav Stat 24(2):146–178
Ronald E, Myers WRH (1978) Probability and statistics for engineers and scientists, 2nd, MacMillan, New York
Tan PN, Steinbach M (2011) Introduction to data mining Posts &telecom press, Jan, pp 139–155
Wang SM, Sun YC (2008) Evaluation of virtual maintenance training in civil airplane based on fuzzy comprehensive evaluation (Chinese). Aircr Des 28(6):42–45
Wang RS, Jia XS, Wang RQ (2006) Study of intelligence frame based on case of battlefield damage assessment (Chinese). Comput Eng 32(7):174–184
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
You, Zf., Liu, Tb., Ding, N., Cui, Kx. (2013). Personnel BDAR Ability Assessment Model Based on Bayesian Stochastic Assessment Method. In: Qi, E., Shen, J., Dou, R. (eds) The 19th International Conference on Industrial Engineering and Engineering Management. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38391-5_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-38391-5_43
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38390-8
Online ISBN: 978-3-642-38391-5
eBook Packages: Business and EconomicsBusiness and Management (R0)