A Catapult. Searching Optima Using Factorial Designs and 2D-Neural Network Mapping Technique - A Tutorial
Natalja Fjodorova, Marjana Novic, Matej Hohnjec
2014
Abstract
The goal of this paper is to represent the feed forward bottle neck neural network (FFBN NN) mapping technique in comparison with traditional statistical method like Factorial Design (FD). Application of both methods provides more information about studied process and enable to establish certificate limits more affectively reaching to best quality and selecting the less cost processes. The represented FFBN NN mapping technique is simple in use, not time consuming and gives 2D visualization of multiple optima in studied technological processes. A catapult design was applied to illustrate the cases and purposes where proposed method can be implemented. The FFBN NN mapping technique can be recommended for use in industries including application in Six Sigma improvement phase.
References
- Changyu, S., Lixia, W., Qian, L., 2007. Optimization of injection molding process parameters using combination of artificial neural network and genetic algorithm method, J. Mater. Process. Technol., vol.183 (2), pp. 412-418.
- Cook, D. F., Ragsdale, C. T., Major, R. L., 2000. Combining a neural network with a genetic algorithm for process parameter optimization, Eng. Appl. Artif. Intell., vol. 13, issue 4, pp.391-396.
- Daszykowski, M., Walczak, B., Massart, D. L., 2003. A journey into low-dimensional spaces with Autoassociative Neural Networks, Talanta, vol. 59 no. 6, pp. 1095-1105.
- Daszykowski, M., Walczak, B., Massart, D. L., 2003. Projection methods in chemistry, Chemom. Intell. Lab. Syst., vol. 65, pp. 97-112.
- Davies, O. L., 1956. Design and Analysis of Industrial Experiments, Imperial Chemical Industries. London.
- Douglas, C. Montgomery, 2012. "Design and Analysis of Experiments, JOHN WILLEY & SONS, INC.New York (NY), 8th Edition.
- Eriksson, L., Johansson, E., Kettaneh-Wold N., et al., 2008. Design of Experiments: Principles and Applications, UMETRICS AB. Sweden, 3-d ed.
- Hamdy, M., Hasan, A., Siren, K., 2011. Applying a multiobjective optimization approach for Design of lowemission cost-effective dwellings. Build. Environ., vol. 46, issue 1, pp.109-123.
- Jin, Y., 2011. Surrogate-assisted evolutionary computation, Recent advances and future challenges. Swarm and Evolutionary Computation, vol.1(2), pp. 61-70.
- Kramer, M. A., 1991. Nonlinear principal component analysis using autoassociative neural networks, AIChE J, vol. 37, no. 2, pp. 233-243.
- Livingstone, D. J., Hesketh, G., Clayworth, D., 1991. Novel methods for the display of.
- multivariate data using neural networks, J. Mol. Graphics, vol. 9, pp. 115-118.
- Loshchilov, I., Schoenauer, M., Sebag, M., 2010. Comparison-Based Optimizers Need ComparisonBased Surrogates. Parallel Problem Solving from Nature (PPSN XI). Springer, vol. 6238, pp. 364-373.
- Natrella, M. G., 1963. Experimental Statistics. National Bureau of Standards Handbook 91, John Wiley and Sons. Inc.Washington.
- Ozcelik, B., Erzurumlu, T., 2006. Comparison of the warpage optimization in the plastic injection molding using ANOVA, neural network model and genetic algorithm, J. Mater. Process. Technol., vol. 171, issue 3, pp. 437-445.
- Park, Y. W., Rhee, S., 2008. Process modeling and parameter optimization using neural network and genetic algorithms for aluminum laser welding automation, The Int. J. Adv. Manuf. Technol., vvol. 37, issue 9-10, pp. 1014-1021.
- Pishvaee, M., Rabbani, M., Torabi, S., 2011. A robust optimization approach to closed-loop supply chain network design under uncertainty. Appl. Math. Modell., vol. 35, issue 2, pp. 637-649.
- Sette, S., Boullart, L., et al., 1997. Optimizing the Fiberto-Yarn Production Process with a Combined Neural Network/Genetic Algorithm Approach, Text. Res. J., vol. 67 (2), pp. 84-92.
- Wu, A., Zhang, J., Chung, H., 2011. Decoupled optimal design for power electronic circuits with adaptive migration in coevolutionary environment. Appl. Soft Comput. J., vol. 11, issue 1, pp. 23-31.
- Wang, J., Zhai, Z. J., Jing,Y., et al., 2010. Particle swarm optimization for redundant building cooling heating and power system. Appl. Energy, vol. 87, issue 12, pp. 3668-3679.
- Zheng, J., Wang, Q., Zhao, P., et al., 2009. Optimization of high-pressure die-casting process parameters using artificial neural network, International journal, advanced manufacturing technology, vol. 44(7-8), pp. 667-674.
Paper Citation
in Harvard Style
Fjodorova N., Novic M. and Hohnjec M. (2014). A Catapult. Searching Optima Using Factorial Designs and 2D-Neural Network Mapping Technique - A Tutorial . In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SDDOM, (SIMULTECH 2014) ISBN 978-989-758-038-3, pages 761-766. DOI: 10.5220/0005108907610766
in Bibtex Style
@conference{sddom14,
author={Natalja Fjodorova and Marjana Novic and Matej Hohnjec},
title={A Catapult. Searching Optima Using Factorial Designs and 2D-Neural Network Mapping Technique - A Tutorial},
booktitle={Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SDDOM, (SIMULTECH 2014)},
year={2014},
pages={761-766},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005108907610766},
isbn={978-989-758-038-3},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SDDOM, (SIMULTECH 2014)
TI - A Catapult. Searching Optima Using Factorial Designs and 2D-Neural Network Mapping Technique - A Tutorial
SN - 978-989-758-038-3
AU - Fjodorova N.
AU - Novic M.
AU - Hohnjec M.
PY - 2014
SP - 761
EP - 766
DO - 10.5220/0005108907610766