The Method of Selection of Parameters’ Values in the Problem of Determining the Quality Level in Manufacturing, Business and Education
Keywords:
Manufacturing Processes Quality, Business Processes Quality, Learning Process Quality, Percentile Function, Non-Parametric Approximation, Generalized Lambda Distribution, Generalized Regression Neural NetworkAbstract
The requirements for production and learning process quality are different in various manufacturing, business and educational organizations. A new approach to fit these requirements and evaluate the closeness of realistic (actual) quality of production or learning processes (based on quality indicators of output or scores of examination tests) is proposed in the paper. The technique uses the strictly defined approximation procedures and allows users automatically evaluate of closeness of actual quality level when changing quality requirements. In case of significant difference between actual and pattern distributions a new approach (using neural network of ‘Generalized Regression Neural Network’ type) of determining the minimum values of the factors that will bring the actual distribution to the pattern one is proposed in the paper.
References
Demir Ahmet, Irakli Rodonaia, Irina Milnikova. On one approach to evaluation of quality level in manufacturing, business and education. Conferences MCSI 2014 and AMCSE 2014, Varna, Bulgaria, September 13-15, 2014
Erceg-Hurn D. M., Mirosevich V. M. Modern Robust Statistical Methods, University of Western Australia, American Psychologist, 2008, Vol. 63, No. 7, 591–601
Fournier B. , Rupin N., Bigerelle M., Najjar D., and Iost A.. “Fitting a Generalized Lambda Distribution uing a percentile-KS (P-KS) Adequcy Criterion”, in (1) Pages 279-309, ISBN: 78-1-58488- 711-9
Gofman A.,.Kelbert M. Un upper bound for Kullback-Leibler divergence with a small number of outliers. Moscow Economics National Research University,2012
Karian Z.A.,. Dudewicz E.J. Handbook of Fitting Statistical Distributions with R, CRC Press, 2011
Manzini R. A. Regattieri H.,Pham E., Ferrari. Maintenance for Industrial , © Springer 2010
Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth. Neural Network Toolbox. User‟s Guide.
The MathWorks, Inc., 3 Apple Hill Drive, Natick, MA, 2014 Max Kuhn . The desirability Package., max.kuhn@p_zer.com., January 7, 2012
Milnikova I. Elaboration of statistical quality control models in education process, PhD thesis, Georgian technical University, Tbilisi, 2012
Perez-Cruz F. Kullback-Leibler Divergence Estimation of Continuous Distributions,Department of Electrical Engineering, Princeton University, New Jersey, 2008
Probabilistic and General Regression Neural Networks. DTREG -Software For Predictive Modeling and Forecasting, User‟s guide , 2013
Rohwer G. Statistical Methods in Sociological Research of Education, Ruhr University Publish,, Germany, 2012.
Schwarz J. Sampling, Regression, Experimental Design and Analysis for Environmental Scientists,Biologists, and Resource Managers, Simon Fraser University, 2011
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