Modified variable return to scale back-propagation neural network robust parameter optimization procedure for multi-quality processes

References

• Adesina, K. A., and S. Daneshvar. 2018. “Integrated Data Envelopment-Thermoexergetic Optimization Framework for Multicomponent Distillation System with Multiexergetic Response in the Robust Parameter Design Procedures.” Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 40 (12): 1491–1507. doi:10.1080/15567036.2018.1477876.
   Web of Science ®Google Scholar
• Adler, N., L. Friedman, and Z. Sinuany-Stern. 2002. “Review of Ranking Methods in the Data Envelopment Analysis Context.” European Journal of Operational Research 140: 249–265. doi: 10.1016/S0377-2217(02)00068-1
   Web of Science ®Google Scholar
• Aggarwal, A., H. Singh, P. Kumar, and M. Singh. 2007. “Optimizing Power Consumption for CNC Turned Parts Using Response Surface Methodology and Taguchi’s Technique-A Comparative Analysis.” Journal of Materials Processing Technology. doi:10.1016/j.jmatprotec.2007.09.041.
   Web of Science ®Google Scholar
• Al-Refaie, A. 2011. “Optimizing Correlated QCHs in Robust Design Using Principal Components Analysis and DEA Techniques.” IIE Transactions (Institute of Industrial Engineering) 44: 262–276. doi:10.1080/09537287.2010.526652.
   Web of Science ®Google Scholar
• Al-Refaie, A. 2012. “Optimizing Performance with Multiple Responses Using Cross-Evaluation and Aggressive Formulation in Data Envelopment Analysis.” IIE Transactions (Institute of Industrial Engineering) 44: 262–276. doi:10.1080/0740817X.2011.566908.
   Web of Science ®Google Scholar
• Al-Refaie, A., and M. D. Al-Tahat. 2011. “Solving the Multi-response Problem in Taguchi Method by Benevolent Formulation in DEA.” Journal of Intelligent Manufacturing. 22: 505–521. doi: 10.1007/s10845-009-0312-8
   Web of Science ®Google Scholar
• Arvidsson, M., and I. Gremyr. 2008. “Principles of Robust Design Methodology.” Quality and Reliability Engineering International 24: 23–35. doi: 10.1002/qre.864
   Web of Science ®Google Scholar
• Balestrassi, P. P., E. Popova, A. P. Paiva, and J. W. Marangnon-Lima. 2009. “Design of Experiments on Neural Network’s Training for Nonlinear Time Series Forecasting.” Neurocomputing 72: 1160–1178. doi: 10.1016/j.neucom.2008.02.002
   Web of Science ®Google Scholar
• Benyounis, K. Y., and A. G. Olabi. 2008. “Optimization of Different Welding Processes Using Statistical and Numerical Approaches – A Reference Guide.” Advances in Engineering Software. 39: 483–496. doi: 10.1016/j.advengsoft.2007.03.012
   Web of Science ®Google Scholar
• Brito, T. G., A. P. Paiva, J. R. Ferreira, J. H. F. Gomes, and P. P. Balestrassi. 2014. “A Normal Boundary Intersection Approach to Multiresponse Robust Optimization of the Surface Roughness in End Milling Process with Combined Arrays.” Precision Engineering 38 (3): 628–638. doi: 10.1016/j.precisioneng.2014.02.013
   Web of Science ®Google Scholar
• Chang, H.-H. 2008. “A Data Mining Approach to Dynamic Multiple Responses in Taguchi Experimental Design.” Expert Systems with Applications 35 (3): 1095–1103. doi: 10.1016/j.eswa.2007.08.005
   Web of Science ®Google Scholar
• Chang, H.-H., and Y.-K. Chen. 2011. “Neuro-genetic Approach to Optimize Parameter Design of Dynamic Multiresponse Experiments.” Applied Soft Computing 11 (1): 436–442. doi: 10.1016/j.asoc.2009.12.002
   Web of Science ®Google Scholar
• Daneshvar, S. 2009. “The Modification of BCC Model Using Facet Analysis.” In Recent Advances in Applied Mathematics: Proceedings of the American Conference on Applied Mathematics, edited by S. Lagakos, L. Perlovsky, M. Jha, B. Covaci, A. Zaharim, and N. Mastorakis, 635–641. WSEAS Press.
   Google Scholar
• Daneshvar, S., G. Izibirak, and A. Javadi. 2014. “Sensitivity Analysis on Modified Variable Returns to Scale Model in Data Envelopment Analysis Using Facet Analysis.” Computer and Industrial Engineering 76: 32–39. doi: 10.1016/j.cie.2014.07.016
   Web of Science ®Google Scholar
• Di Scala, K., G. Meschino, A. Vega-Gaalvez, R. Lemus-Mondaca, S. Rousa, and R. Mascheroni. 2013. “An Artificial Neural Network Model for Prediction of Quality Characteristics of Apples During Convective Dehydration.” Food Science and Technology 33 (3): 411–416. doi: 10.1590/S0101-20612013005000064
   Web of Science ®Google Scholar
• Gomes, J. H. F., A. P. Paiva, S. C. Costa, P. P. Balestrassi, and E. J. Paiva. 2013. “Weighted Multivariate Mean Square Error for Processes Optimization: A Case Study on Fluxcored Arc Welding for Stainless Steel Claddings.” European Journal of Operational Research 226 (3): 522–535. doi: 10.1016/j.ejor.2012.11.042
   Web of Science ®Google Scholar
• Gutiérrez, E., and S. Lozano. 2010. “Data Envelopment Analysis of Multiple Response Experiments.” Applied Mathematical Modelling 34: 1139–1148. doi: 10.1016/j.apm.2009.08.001
   Web of Science ®Google Scholar
• Jeyapaul, R., P. Shahabudeen, and K. Krishnaiah. 2006. “Simultaneous Optimization of Multi-response Problems in the Taguchi Method Using Genetic Algorithm.” International Journal of Advanced Manufacturing Technology 30: 870–878. doi: 10.1007/s00170-005-0095-9
   Web of Science ®Google Scholar
• Li, Y., and L. Zhu. 2017. “Optimization of Product Form Design Using Fuzzy Integral-Based Taguchi Method.” Journal of Engineering Design 28 (7–9): 480–504. doi: 10.1080/09544828.2017.1346239
   Web of Science ®Google Scholar
• Liao, H.-C. 2004. “A Data Envelopment Analysis Method for Optimizing Multi-response Problem with Censored Data in the Taguchi Method.” Computer and Industrial Engineering 46: 817–835. doi: 10.1016/j.cie.2004.05.012
   Web of Science ®Google Scholar
• Liao, H.-C., and Y.-K. Chen. 2002. “Optimizing Multi-Response Problem in the Taguchi Method by DEA Based Ranking Method.” Journal of Quality and Reliability Management 19 (7): 825–837. doi: 10.1108/02656710210434766
   Google Scholar
• Gutierrez, E., and S. Lozano. 2010. “Data Envelopment of Multiple Response Experiments.” Applied Mathematical Modeling 34: 1139–1148. doi: 10.1016/j.apm.2009.08.001
   Web of Science ®Google Scholar
• Ma, R., L. Yao, M. Jin, and P. Ren. 2014. “The DEA Game Cross-Efficiency Model for Supplier Selection Problem Under Competition.” Applied Mathematics & Information Sciences 8: 811–818. doi:10.12785/amis/080242.
   Google Scholar
• Montgomery, D. C. 2009. Design and Analysis of Experiments. 7th ed. New York: John Wiley.
   Google Scholar
• Myers, R. H., D. C. Montgomery, G. G. Vining, C. M. Borror, and S. M. Kowalski. 2004. “Response Surface Methodology: A Retrospective and Literature Survey.” Journal of Quality Technology 36: 53–77. doi: 10.1080/00224065.2004.11980252
   Web of Science ®Google Scholar
• Noorossana, R., T. Davanloo, and A. Saghaei. 2009. “An Artificial Neural Network Approach to Multiple-Response Optimization.” The International Journal of Advanced Manufacturing Technology 40 (11–12): 1227–1238. doi: 10.1007/s00170-008-1423-7
   Web of Science ®Google Scholar
• Rao, R. S., C. G. Kumar, R. S. Prakasham, and P. J. Hobbs. 2008. “The Taguchi Methodology as a Statistical Tool for Biotechnological Applications: A Critical Appraisal.” Biotechnology Journal 3: 510–523. doi: 10.1002/biot.200700201
   PubMedGoogle Scholar
• Raza, Zulfiqar A., N. Ahmad, and S. Kamal. 2014. “Multi-response Optimization of Rhamnolipid Production Using Grey Rational Analysis in Taguchi Method.” Biotechnology Reports 3: 86–94. doi: 10.1016/j.btre.2014.06.007
   Google Scholar
• Rivero, R., and M. Garcia. 2001. “Exergy Analysis of a Reactive Distillation MTBE Unit.” International Journal of Thermodynamics 4 (2): 85–92.
   Google Scholar
• Robinson, T. J., C. M. Borror, and R. H. Myers. 2004. “Robust Parameter Design: A Review.” Quality and Reliability Engineering International 20: 81–101. doi: 10.1002/qre.602
   Web of Science ®Google Scholar
• Rocha, L. C. S., A. P. Paiva, E. J. Paiva, and P. P. Balestrassi. 2016. “Comparing DEA and Principal Component Analysis in the Multiobjective Optimization of P-GMAW Process.” Journal of the Brazilian Society of Mechanical Sciences and Engineering 38: 2513–2526. doi:10.1007/s40430-015-0355-z.
   Web of Science ®Google Scholar
• Salmasnia, A., R. B. Kazemzadeh, and M. M. Tabrizi. 2012. “A Novel Approach for Optimization of Correlated Multiple Responses Based on Desirability Function and Fuzzy Logics.” Neurocomputing 91: 56–66. doi: 10.1016/j.neucom.2012.03.001
   Web of Science ®Google Scholar
• Salmasnia, A., M. Bastan, and A. Moeini. 2012. “A Robust Intelligent Framework for Multiple Response Statistical Optimization Problems Based on Artificial Neural Network and Taguchi Method.” International Journal of Quality, Statistics and Reliability. doi:10.1155/2012/494818.
   Google Scholar
• Sibalija, T. V., S. Z. Petronic, V. D. Majstorovic, R. Prokic-Cvetkovic, and A. Milosavljevic. 2011. “Multi-response Design of Nd:YAG Laser Drilling of Ni-Based Superalloy Sheets Using Taguchi’s Quality Loss Function, Multivariate Statistical Methods and Artificial Intelligence.” The International Journal of Advanced Manufacturing Technology 54 (5–8): 537–552. doi: 10.1007/s00170-010-2945-3
   Web of Science ®Google Scholar
• Stathalkis, D. 2009. “How Many Hidden Layers and Nodes?” International Journal of Remote Sensing 30 (8): 2133–2147. doi: 10.1080/01431160802549278
   Web of Science ®Google Scholar
• Su, C. T., and L. I. Tong 1997. “Multi-response Robust Design by Principal Component Analysis.” Total Quality Management 8 (6): 409–416. doi: 10.1080/0954412979415
   Web of Science ®Google Scholar
• Taguchi, G., S. Chowdhury, and Y. Wu. 2005. Taguchi's Quality Engineering Handbook. Hoboken, NJ: John Wiley.
   Google Scholar
• Wu, F.-C., and C.-C. Chyu. 2002. “A Comparative Study on Taguchi’s SN Ratio, Minimising MSD and Variance for Nominal-the-Best Characteristic Experiment.” The International Journal of Advanced Manufacturing Technology. 20: 655–659. doi: 10.1007/s001700200203
   Web of Science ®Google Scholar