Advertisement

Towards Improving the Applicability of Non-parametric Multiple Comparisons to Select the Best Soft Computing Models in Rubber Extrusion Industry

  • Ruben Urraca-Valle
  • Enrique Sodupe-Ortega
  • Alpha Pernía-Espinoza
  • Andres Sanz-Garcia
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)

Abstract

In this paper we propose different strategies to apply non-parametric multiple comparisons in industrial environments. These techniques have been widely used in theoretical studies and research to evaluate the performance of models, but they are still far from being implemented in real applications. So, we develop three new automatized strategies to ease the selection of soft computing models using data from industrial processes. A rubber products manufacturer was selected as a real industry to conduct the experiments. More specifically, we focus our study on the mixing phase. The rheology curve of rubber compounds is predicted to anticipate possible failures in the vulcanization process. More accurate predictions are needed to provide set points to enhance the control the process, particularly working in this rapidly changing environment. Selecting among a wide range of models increases the probability of achieving the best predictions. The main goal of our methodology is therefore to automatize the selection process when many choices are availables. The models based on soft computing used to validate our proposal are neural networks and support vector machines and also other alternatives such as linear and rule-based models.

Keywords

Support Vector Machine Multilayer Perceptron Non-parametric comparison Friedman Rubber Mixing Process 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bradley, J.B.: Neural networks: A comprehensive foundation. Information Processing & Management 31(5), 786–794 (1995)CrossRefGoogle Scholar
  2. 2.
    Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)CrossRefGoogle Scholar
  3. 3.
    Corchado, E., Abraham, A., Carvalho, A.: Hybrid intelligent algorithms and applications. Information Sciences 180(14), 2633–2634 (2010)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Corchado, E., Graña, M., Woźniak, M.: New trends and applications on hybrid artificial intelligence systems. Neurocomputing 75(1), 61–63 (2012)CrossRefGoogle Scholar
  5. 5.
    Corchado, E., Herrero, Á.: Neural visualization of network traffic data for intrusion detection. Applied Soft Computing 11(2), 2042–2056 (2011)CrossRefGoogle Scholar
  6. 6.
    Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1(1), 3–18 (2011)CrossRefGoogle Scholar
  7. 7.
    Marcos, A., Espinoza, A., Elas, F., Forcada, A.: A neural network-based approach for optimising rubber extrusion lines. International Journal of Computer Integrated Manufacturing 20(8), 828–837 (2007)CrossRefGoogle Scholar
  8. 8.
    Quinlan: Combining instance-based and model-based learning. In: Proceedings of the Tenth International Conference on Machine Learning, pp. 236–243 (1993)Google Scholar
  9. 9.
    Wilcoxon, F.: Individual comparisons by ranking methods. Biometrics Bulletin 1(6), 80–83 (1945)CrossRefGoogle Scholar
  10. 10.
    Wilkinson, G.N., Rogers, C.E.: Symbolic description of factorial models for analysis of variance. Journal of the Royal Statistical Society. Series C (Applied Statistics) 22, 392–399 (1973)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ruben Urraca-Valle
    • 1
  • Enrique Sodupe-Ortega
    • 1
  • Alpha Pernía-Espinoza
    • 1
  • Andres Sanz-Garcia
    • 1
  1. 1.EDMANS Research GroupUniversity of La RiojaLogroñoSpain

Personalised recommendations