- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Advances in Artificial Neural Systems
Volume 2013 (2013), Article ID 628313, 9 pages
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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Matlab User’s Guide: Neural Network Toolbox Version 2010, MathWorks, Natick, MA, USA, 2010.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.