About this Journal Submit a Manuscript Table of Contents
Advances in Artificial Neural Systems
Volume 2013 (2013), Article ID 628313, 9 pages
http://dx.doi.org/10.1155/2013/628313
Research Article

Desirability Improvement of Committee Machine to Solve Multiple Response Optimization Problems

1Department of Mechanical and Manufacturing Engineering, Universiti Putra Malaysia, 43400 Serdang, Selangor, Malaysia
2Faculty of Engineering, Tarbiat Modares University, P.O. Box 14155-143, Tehran 1411713116, Iran

Received 28 February 2013; Accepted 8 August 2013

Academic Editor: Kyong Joo Oh

Copyright © 2013 Seyed Jafar Golestaneh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. J. Golestaneh, N. Ismail, S. H. Tang, M. K. A. M. Ariffin, H. Moslemi Naeini, and A. A. Maghsoudi, “A committee machine approach to multiple response optimization,” International Journal of Physical Sciences, vol. 6, no. 35, pp. 7935–7949, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. E. Del Castillo, D. C. Montgomery, and D. R. McCarville, “Modified desirability functions for multiple response optimization,” Journal of Quality Technology, vol. 28, no. 3, pp. 337–345, 1996. View at Scopus
  3. W. Guo, Y. Zhang, J. Lu et al., “Optimization of fermentation medium for nisin production from Lactococcus lactis subsp. lactis using response surface methodology (RSM) combined with artificial neural network-genetic algorithm (ANN-GA),” African Journal of Biotechnology, vol. 9, no. 38, pp. 6264–6272, 2010. View at Scopus
  4. J. Antony, R. B. Anand, M. Kumar, and M. K. Tiwari, “Multiple response optimization using Taguchi methodology and neuro-fuzzy based model,” Journal of Manufacturing Technology Management, vol. 17, no. 7, pp. 908–925, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Chang, “A data mining approach to dynamic multiple responses in Taguchi experimental design,” Expert Systems with Applications, vol. 35, no. 3, pp. 1095–1103, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. S. Kumanan, J. E. R. Dhas, and K. Gowthaman, “Determination of submerged arc welding process parameters using Taguchi method and regression analysis,” Indian Journal of Engineering and Materials Sciences, vol. 14, no. 3, pp. 177–183, 2007. View at Scopus
  7. A. W. L. Yao, H. T. Liao, and C. Y. Liu, “A taguchi and neural network based electric load demand forecaster,” The Open Automation and Control System Journal, vol. 1, pp. 7–13, 2008.
  8. D. Lepadatu, A. Kobi, R. Hambli, and A. Barreau, “Lifetime multiple response optimization of metal extrusion die,” in Proceedings of the Annual Reliability and Maintainability Symposium (RAMS '05), pp. 37–42, January 2005. View at Scopus
  9. S. H. R. Pasandideh and S. T. A. Niaki, “Multi-response simulation optimization using genetic algorithm within desirability function framework,” Applied Mathematics and Computation, vol. 175, no. 1, pp. 366–382, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. I. Mukherjee and P. K. Ray, “A modified tabu search strategy for multiple-response grinding process optimisation,” International Journal of Intelligent Systems Technologies and Applications, vol. 4, no. 1-2, pp. 97–122, 2008.
  11. R. Noorossana, S. Davanloo Tajbakhsh, and A. Saghaei, “An artificial neural network approach to multiple-response optimization,” International Journal of Advanced Manufacturing Technology, vol. 40, no. 11-12, pp. 1227–1238, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Cheng, C.-J. Cheng, and E. S. Lee, “Neuro-fuzzy and genetic algorithm in multiple response optimization,” Computers and Mathematics with Applications, vol. 44, no. 12, pp. 1503–1514, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. P. Chatsirirungruang and M. Miyakawa, “Application of genetic algorithm to numerical experiment in robust parameter design for signal multi-response problem,” International Journal of Management Science and Engineering Management, vol. 4, no. 1, pp. 49–59, 2009.
  14. T. Kamo and C. Dagli, “Hybrid approach to the Japanese candlestick method for financial forecasting,” Expert Systems with Applications, vol. 36, no. 3, pp. 5023–5030, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. H. B. Celikoglu, “Application of radial basis function and generalized regression neural networks in non-linear utility function specification for travel mode choice modelling,” Mathematical and Computer Modelling, vol. 44, no. 7-8, pp. 640–658, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. Matlab User’s Guide: Neural Network Toolbox Version 2010, MathWorks, Natick, MA, USA, 2010.
  17. E. Ardil and P. S. Sandhu, “A soft computing approach for modeling of severity of faults in software systems,” International Journal of Physical Sciences, vol. 5, no. 2, pp. 074–085, 2010. View at Scopus
  18. J. Bo, T. Yuchun, and Z. Yan-Qing, “Hybrid SVM-ANFIS for protein subcellular location prediction,” International Journal of Computational Intelligence in Bioinformatics and Systems Biology, vol. 1, no. 1, p. 59, 2009.
  19. N. Ismail, S. J. Golestaneh, S. H. Tang et al., “Modified committee neural networks for prediction of machine failure times,” in Proceedings of the 3rd national intelligent systems and information technology symposium (ISITS '10), 2010.
  20. A. Kadkhodaie-Ilkhchi, M. R. Rezaee, and H. Rahimpour-Bonab, “A committee neural network for prediction of normalized oil content from well log data: an example from South Pars Gas Field, Persian Gulf,” Journal of Petroleum Science and Engineering, vol. 65, no. 1-2, pp. 23–32, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. S. Karimpouli, N. Fathianpour, and J. Roohi, “A new approach to improve neural networks' algorithm in permeability prediction of petroleum reservoirs using supervised committee machine neural network (SCMNN),” Journal of Petroleum Science and Engineering, vol. 73, no. 3-4, pp. 227–232, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. K. Y. Benyounis, A. G. Olabi, and M. S. J. Hashmi, “Multi-response optimization of CO2 laser-welding process of austenitic stainless steel,” Optics and Laser Technology, vol. 40, no. 1, pp. 76–87, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. C. Cojocaru, M. Khayet, G. Zakrzewska-Trznadel, and A. Jaworska, “Modeling and multi-response optimization of pervaporation of organic aqueous solutions using desirability function approach,” Journal of Hazardous Materials, vol. 167, no. 1–3, pp. 52–63, 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. G. Martinez Delfa, A. Olivieri, and C. E. Boschetti, “Multiple response optimization of styrene-butadiene rubber emulsion polymerization,” Computers and Chemical Engineering, vol. 33, no. 4, pp. 850–856, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. D. S. Nagesh and G. L. Datta, “Genetic algorithm for optimization of welding variables for height to width ratio and application of ANN for prediction of bead geometry for TIG welding process,” Applied Soft Computing Journal, vol. 10, no. 3, pp. 897–907, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. A. Patnaik and S. Biswas, “An evolutionary approach to parameter optimisation of submerged arc welding in the hardfacing process,” International Journal of Manufacturing Research, vol. 2, no. 4, pp. 462–483, 2007.
  27. C. Pizarro, J. M. González-Sáiz, and N. Pérez-del-Notario, “Multiple response optimisation based on desirability functions of a microwave-assisted extraction method for the simultaneous determination of chloroanisoles and chlorophenols in oak barrel sawdust,” Journal of Chromatography A, vol. 1132, no. 1-2, pp. 8–14, 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. P. C. Giordano, H. D. Martínez, A. A. Iglesias, A. J. Beccaria, and H. C. Goicoechea, “Application of response surface methodology and artificial neural networks for optimization of recombinant Oryza sativa non-symbiotic hemoglobin 1 production by escherichia coli in medium containing byproduct glycerol,” Bioresource Technology, vol. 101, no. 19, pp. 7537–7544, 2010. View at Publisher · View at Google Scholar · View at Scopus
  29. M. S. Bhatti, A. S. Reddy, R. K. Kalia, and A. K. Thukral, “Modeling and optimization of voltage and treatment time for electrocoagulation removal of hexavalent chromium,” Desalination, vol. 269, no. 1–3, pp. 157–162, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Aggarwal, H. Singh, P. Kumar, and M. Singh, “Optimization of multiple quality characteristics for CNC turning under cryogenic cutting environment using desirability function,” Journal of Materials Processing Technology, vol. 205, no. 1-3, pp. 42–50, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. K. Y. Benyounis and A. G. Olabi, “Optimization of different welding processes using statistical and numerical approaches—a reference guide,” Advances in Engineering Software, vol. 39, no. 6, pp. 483–496, 2008. View at Publisher · View at Google Scholar · View at Scopus
  32. H. Chang and Y. Chen, “Neuro-genetic approach to optimize parameter design of dynamic multiresponse experiments,” Applied Soft Computing Journal, vol. 11, no. 1, pp. 436–442, 2011. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Liang, “Combining neural networks and genetic algorithms for predicting the reliability of repairable systems,” International Journal of Quality and Reliability Management, vol. 25, no. 2, pp. 201–210, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. L. Tian and A. Noore, “Evolutionary neural network modeling for software cumulative failure time prediction,” Reliability Engineering and System Safety, vol. 87, no. 1, pp. 45–51, 2005. View at Publisher · View at Google Scholar · View at Scopus
  35. A. H. Brie and P. Morignot, “Genetic planning using variable length chromosomes,” in Proceedings of the International Conference on Automated Planning and Scheduling/Artificial Intelligence Planning Systems (ICAPS/AIPS '05)., 2005.
  36. U. S. Dixit and S. Chandra, “A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold flat rolling process,” International Journal of Advanced Manufacturing Technology, vol. 22, no. 11-12, pp. 883–889, 2003. View at Publisher · View at Google Scholar · View at Scopus
  37. A. Haghizadeh, L. T. Shui, and E. Goudarzi, “Estimation of yield sediment using artificial neural network at basin scale,” Australian Journal of Basic and Applied Sciences, vol. 4, no. 7, pp. 1668–1675, 2010. View at Scopus
  38. P. Krause, D. P. Boyle, and F. Bäse, “Comparison of different efficiency criteria for hydrological model assessment,” Advances in Geosciences, vol. 5, pp. 89–97, 2005. View at Scopus
  39. S. Banik, M. Anwer, and A. F. M. K. Khan, “Predictive power of the daily Bangladeshi exchange rate series based on markov model, neuro fuzzy model and conditional heteroskedastic model,” in Proceedings of the 12th International Conference on Computer and Information Technology (ICCIT '09), pp. 303–308, December 2009. View at Publisher · View at Google Scholar · View at Scopus