Advertisement

Soft Computing Decision Support for a Steel Sheet Incremental Cold Shaping Process

  • José Ramon Villar
  • Javier Sedano
  • Emilio Corchado
  • Laura Puigpinós
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6936)

Abstract

It is known that the complexity inherited in most of the new real world problems, for example, the cold rolled steel industrial process, increases as the computer capacity does. Higher performance requirements with a lower amount of data samples are needed due to the costs of generating new instances, specially in those processes where new technologies arise. This study is focused on the analysis and design of a novel decision support system for an incremental steel cold shaping process, where there is a lack of knowledge of which operating conditions are suitable for obtaining high quality results. The most suitable features have been found using a wrapper feature selection method, in which genetic algorithms and neural networks are hybridized. Some facts concerning the enhanced experimentation needed and the improvements in the algorithm are drawn.

Keywords

Wrapper Feature Selection Genetic Algorithms Neural Networks Support Vector Machines Incremental Cold Shaping 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altun, A.A., Allahverdi, N.: Neural network based recognition by using genetic algorithm for feature selection of enhanced fingerprints. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds.) ICANNGA 2007, Part II. LNCS, vol. 4432, pp. 467–476. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Casillas, J., Cordón, O., del Jesus, M.J., Herrera, F.: Genetic Feature Selection in a Fuzzy Rule-Based Classification System Learning Process. Information Sciences 136(1-4), 135–157 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Corchado, E., MacDonald, D., Fyfe, C.: Maximum and Minimum Likelihood Hebbian Learning for Exploratory Projection Pursuit. Data Min. Knowl. Discov. 8(3), 203–225 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Corchado, E., Sedano, J., Curiel, L., Villar, J.R.: Optimizing the operating conditions in a high precision industrial process using soft computing techniques. Expert Systems (2011) (in press) Google Scholar
  5. 5.
    de la Cal, E., Fernández, E.M., Quiroga, R., Villar, J., Sedano, J.: Scalability of a Methodology for Generating Technical Trading Rules with GAPs Based on Risk-Return Adjustment and Incremental Training. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds.) HAIS 2010. LNCS, vol. 6077, pp. 143–150. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Fung, G.M., Mangasarian, O.L.: A Feature Selection Newton Method for Support Vector Machine Classification. Computational Optimization and Applications 28(2), 185–202 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Huanga, C.-L., Wang, C.-J.: A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with Applications 31(2), 231–240 (2006)CrossRefGoogle Scholar
  8. 8.
    The MathWorks, MATLAB - The Language Of Technical Computing (2011), http://www.mathworks.com/products/matlab/
  9. 9.
    Mohanty, I., Datta, S., Bhattacharjeeb, D.: Composition-Processing-Property Correlation of Cold-Rolled IF Steel Sheets Using Neural Network. Materials and Manufacturing Processes 24(1), 100–105 (2009)CrossRefGoogle Scholar
  10. 10.
    Sedano, J., Corchado, E., Curiel, L., Villar, J., Bravo, P.: The Application of a Two-Step AI Model to an Automated Pneumatic Drilling Process. Int. J. of Comp. Mat. 86(10-11), 1769–1777 (2008)CrossRefzbMATHGoogle Scholar
  11. 11.
    Sedano, J., Curiel, L., Corchado, E., de la Cal, E., Villar, J.R.: A Soft Computing Based Method for Detecting Lifetime Building Thermal Insulation Failures. Int. Comp.-Aided Eng. 17(2), 103–115 (2009)Google Scholar
  12. 12.
    Sedano, J., Villar, J.R., Curiel, L., de la Cal, E., Corchado, E.: Improving Energy Efficiency in Buildings Using Machine Intelligence. In: Corchado, E., Yin, H. (eds.) IDEAL 2009. LNCS, vol. 5788, pp. 773–782. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Villar, J.R., Suárez, M.R., Sedano, J., Mateos, F.: Unsupervised Feature Selection in High Dimensional Spaces and Uncertainty. In: Corchado, E., Wu, X., Oja, E., Herrero, Á., Baruque, B. (eds.) HAIS 2009. LNCS, vol. 5572, pp. 565–572. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Villar, J.R., Berzosa, A., de la Cal, E., Sedano, J., García-Tamargo, M.: Multi-objective Simulated Annealing in Genetic Algorithm and Programming learning with low quality data. In: Publication for Neural Computing (2011)Google Scholar
  15. 15.
    Wong, M.L.D., Nandi, A.K.: Automatic digital modulation recognition using artificial neural network and genetic algorithm. Signal Proc. 84(2), 351–365 (2004)CrossRefzbMATHGoogle Scholar
  16. 16.
    Yang, J., Honavar, V.: Feature Subset Selection Using a Genetic Algorithm. IEEE Intelligent Systems 13(2), 44–49 (1998)CrossRefGoogle Scholar
  17. 17.
    Zhang, P., Verma, B., Kumar, K.: Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection. Pat. Recog. Letters 28(7), 909–919 (2005)CrossRefGoogle Scholar
  18. 18.
    Zhao, J., Cao, H.Q., Ma, L.X., Wang, F.Q., Li, S.B.: Study on intelligent control technology for the deep drawing of an axi-symmetric shell part. J. of Materials Processing Tech. 151(1-3), 98–104 (2005)CrossRefGoogle Scholar
  19. 19.
    Zhao, J., Wang, F.: Parameter identification by neural network for intelligent deep drawing of axisymmetric workpieces. J. of Materials Processing Tech. 166(3), 387–391 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José Ramon Villar
    • 1
  • Javier Sedano
    • 2
  • Emilio Corchado
    • 3
  • Laura Puigpinós
    • 4
  1. 1.University of OviedoGijónSpain
  2. 2.Instituto Tecnoló de Castilla y LeónPoligono Industrial de VillalonquejarBurgosSpain
  3. 3.Computer Science and Automatica DepartmentUniversity of SalamancaSalamancaSpain
  4. 4.Fundación Privada AscammCerdanyola del VallésSpain

Personalised recommendations