Using Combination of Optimized Recurrent Neural Network with Design of Experiments and Regression for Control Chart Forecasting
recurrent neural network (RNN) is an efficient tool for
modeling production control process as well as modeling services. In
this paper one RNN was combined with regression model and were
employed in order to be checked whether the obtained data by the
model in comparison with actual data, are valid for variable process
control chart. Therefore, one maintenance process in workshop of
Esfahan Oil Refining Co. (EORC) was taken for illustration of
models. First, the regression was made for predicting the response
time of process based upon determined factors, and then the error
between actual and predicted response time as output and also the
same factors as input were used in RNN. Finally, according to
predicted data from combined model, it is scrutinized for test values
in statistical process control whether forecasting efficiency is
acceptable. Meanwhile, in training process of RNN, design of
experiments was set so as to optimize the RNN.
[1] D. MONTGOMERY, "Control estadístico de la calidad", 2004, 3rd Ed.
Limusa Wiley, Mexico, pp.797.
[2] J. A. Vazquez-Lopez, I. Lopez-Juarez, M. Pe├▒a-Cabrera, " On the Use of
the Fuzzy ARTMAP Neural Network for Pattern Recognition in
Statistical Process Control using a Factorial Design ", Int. J. of
Computers, Communications & Control, ISSN 1841-9836, Vol. V
(2010), No. 2, pp. 205-215.
[3] A.J. MORRIS, G.A. MONTAGUE, M.J. WILLIS, "Artificial neural
networks : studies in process modeling and control", 1994, Chem. Eng.
Res. Des. 72(A1), pp. 3-19.
[4] D.F. COOK, C.T. RAGSDALE, R.L. MAJOR, "Combining a neural
network with a genetic algorithm for process parameter optimization",
2000, Eng.Appl.Artif. Intell. 13, pp. 391-396.
[5] L.R. MedsKer, L.C. Jain, "Recurrent Neural Networks : Design and
Application", CRC Press, Boca Raton London New York Washington,
D.C., 2001.
[6] L.G. Esteban, F. García Fernández, P. de Palacios, M. Conde, "Artificial
neural networks in variable process control: application in particleboard
manufacture", Sistemas y Recursos Forestales 2009 18(1), pp. 92-100.
[7] D.F. COOK, C.C. CHIU, "Predicting the internal bond strength of
particleboard: utilizing a radial basis function neural network", 1997,
Eng. Appl. Artif. Intell. 10 (2), pp. 171-177.
[8] S. MALINOV, W. SHA, J.J. MCKEOWN, "Modelling the correlation
between processing parameters and properties in titanium alloys using
artificial neural network", 2001, Comput. Mater. Sci. 21, pp. 375-394.
[9] W. Laosiritaworn, N. Chotchaithanakorn, "Artificial Neural Networks
Parameters Optimization with Design of Experiments: An Application in
Ferromagnetic Materials Modeling", Chiang Mai J. Sci. 2009; 36(1),
pp.83-91.
[10] M.S. Packianather, P.R. Drake, H. Rowland, "Optimizing the Parameters
of Multilayered Feed Forward Neural Networks through Taguchi Design
of Experiments", Qual. Reliab. Eng. Int., 2000; 16: pp. 461-473.
[1] D. MONTGOMERY, "Control estadístico de la calidad", 2004, 3rd Ed.
Limusa Wiley, Mexico, pp.797.
[2] J. A. Vazquez-Lopez, I. Lopez-Juarez, M. Pe├▒a-Cabrera, " On the Use of
the Fuzzy ARTMAP Neural Network for Pattern Recognition in
Statistical Process Control using a Factorial Design ", Int. J. of
Computers, Communications & Control, ISSN 1841-9836, Vol. V
(2010), No. 2, pp. 205-215.
[3] A.J. MORRIS, G.A. MONTAGUE, M.J. WILLIS, "Artificial neural
networks : studies in process modeling and control", 1994, Chem. Eng.
Res. Des. 72(A1), pp. 3-19.
[4] D.F. COOK, C.T. RAGSDALE, R.L. MAJOR, "Combining a neural
network with a genetic algorithm for process parameter optimization",
2000, Eng.Appl.Artif. Intell. 13, pp. 391-396.
[5] L.R. MedsKer, L.C. Jain, "Recurrent Neural Networks : Design and
Application", CRC Press, Boca Raton London New York Washington,
D.C., 2001.
[6] L.G. Esteban, F. García Fernández, P. de Palacios, M. Conde, "Artificial
neural networks in variable process control: application in particleboard
manufacture", Sistemas y Recursos Forestales 2009 18(1), pp. 92-100.
[7] D.F. COOK, C.C. CHIU, "Predicting the internal bond strength of
particleboard: utilizing a radial basis function neural network", 1997,
Eng. Appl. Artif. Intell. 10 (2), pp. 171-177.
[8] S. MALINOV, W. SHA, J.J. MCKEOWN, "Modelling the correlation
between processing parameters and properties in titanium alloys using
artificial neural network", 2001, Comput. Mater. Sci. 21, pp. 375-394.
[9] W. Laosiritaworn, N. Chotchaithanakorn, "Artificial Neural Networks
Parameters Optimization with Design of Experiments: An Application in
Ferromagnetic Materials Modeling", Chiang Mai J. Sci. 2009; 36(1),
pp.83-91.
[10] M.S. Packianather, P.R. Drake, H. Rowland, "Optimizing the Parameters
of Multilayered Feed Forward Neural Networks through Taguchi Design
of Experiments", Qual. Reliab. Eng. Int., 2000; 16: pp. 461-473.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:61443", author = "R. Behmanesh and I. Rahimi", title = "Using Combination of Optimized Recurrent Neural Network with Design of Experiments and Regression for Control Chart Forecasting", abstract = "recurrent neural network (RNN) is an efficient tool for
modeling production control process as well as modeling services. In
this paper one RNN was combined with regression model and were
employed in order to be checked whether the obtained data by the
model in comparison with actual data, are valid for variable process
control chart. Therefore, one maintenance process in workshop of
Esfahan Oil Refining Co. (EORC) was taken for illustration of
models. First, the regression was made for predicting the response
time of process based upon determined factors, and then the error
between actual and predicted response time as output and also the
same factors as input were used in RNN. Finally, according to
predicted data from combined model, it is scrutinized for test values
in statistical process control whether forecasting efficiency is
acceptable. Meanwhile, in training process of RNN, design of
experiments was set so as to optimize the RNN.", keywords = "RNN, DOE, regression, control chart.", volume = "6", number = "1", pages = "82-5", }