Skip to main content
SpringerLink
Log in

A Radial Basis Function Redesigned for Predicting a Welding Process

  • Conference paper
Advances in Soft Computing (MICAI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6438))

Included in the following conference series:

Abstract

Neural Networks (NNs) have been widely used in many industrial processes for prediction and optimization and they have been proven to be useful tools for explaining complex processes. The main objective of this work consists of improving the accuracy of a Radial Basis Function Neural Network Redesigned by Genetic Algorithm and Mahalanobis distance for predicting a welding process. The evaluation function in this approach considers the use of the Coefficient of Determination R 2. The results indicated that the statistical method R 2 is a good alternative to validate the efficiency of the Neural Network model. The principal conclusion in this work is that the Radial Basis Function Redesigned by Genetic Algorithm and Mahalanobis distance had a very good performance in a real case, considering the prediction of specific responses in a welding process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arbib Michael, A.: The Handbook of Brain Theory and Neural Networks. The MIT Press, London (2003)

    MATH  Google Scholar 

  2. Benyounis, K.Y., Olabi, A.G.: Optimization of different welding process using statistical and numerical approaches – A reference guide. Advances in Engineering Software 39, 483–496 (2008)

    Article  Google Scholar 

  3. Billings Steve, A., Zheng Guang, L.: Radial Basis Function Network Configuration Using Genetic Algorithms. Neural Networks 8(6), 877–890 (1995)

    Article  Google Scholar 

  4. Cepeda, F.: Análisis y optimización de parámetros empleados en la unión y tratamientos superficiales mediante laser CO2 y Nd-YAG en la aleación de cobalto ASTM F-75. Master Thesis. COMIMSA (2010)

    Google Scholar 

  5. Ding, H., Xiao, Y., Yue, J.: Adaptive Training of Radial Basis Function Networks using Particle Swarm Optimization Algorithm. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3610, pp. 119–128. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  6. Freeman, J.A., Skapura, D.M.: Redes Neuronales. In: Algoritmos, aplicaciones y técnicas de propagación, p. 306. Addison-Wesley, México (1993)

    Google Scholar 

  7. Freeman, J.A., Skapura, D.M.: Neural Networks. In: Algorithms, applications, and Programming Techniques, Addison-Wesley, Reading (1991)

    Google Scholar 

  8. Goldberg David, E.: Genetic algorithms in search, optimization, and machine learning. Oxford University Press, New York (1989)

    MATH  Google Scholar 

  9. Goldberg David, E.: The design of Innovation, Genetic Algorithms and Evolutionary Computation. Kluwer Academic Publishers, USA (2002)

    Google Scholar 

  10. Gregorcic, G., Lightbody, G.: Nonlinear system identification: From multiple-model networks to Gaussian processes. Engineering Applications of Artificial Intelligence (2008)

    Google Scholar 

  11. Hagan, Demuth, Beale: Neural Network Design. Thomson Publishing Inc., USA (1996)

    Google Scholar 

  12. Härdle, W., Simar, L.: Applied Multivariate Statistical Analysis, 2nd edn. Springer, Heidelberg (2007)

    MATH  Google Scholar 

  13. Haykin, S.: Neural Networks: A comprehensive foundation. Prentice-Hall, Englewood Cliffs (1999)

    MATH  Google Scholar 

  14. Holland John, H.: Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann Arbor (1975)

    MATH  Google Scholar 

  15. Martín del Brío, B., Sanz, A.: Redes Neuronales y Sistemas Difusos. Alfaomega. España. (2007)

    Google Scholar 

  16. Meqin, L., Jida, C.: Determining the structures and parameters of Radial Basis Function Neural Networks using improved Genetic Algorithms. Journal CSUT 5(2), 141–146 (1998)

    Google Scholar 

  17. Meziane, F., Vadera, S., Kobbacy, K., Proudlove, N.: Intelligent systems in manufacturing: current developments and future prospects. Journal Integrated Manufacturing Systems 11(4), 218–238 (2000)

    Article  Google Scholar 

  18. Mitsuo, G., Chen, R.: Genetic Algorithms & Engineering Optimization. John Wiley & Sons, Chichester (2000)

    Google Scholar 

  19. Montgomery, D.C., Peck, E.A., Vining, G.G.: Introducción al Análisis de Regresión Lineal. In: Editorial Continental, Tercera edición, México (2006)

    Google Scholar 

  20. Nelles, O.: Nonlinear System Identification: From classical approaches to neural networks and fuzzy models. Springer, Heidelberg (2001)

    Book  MATH  Google Scholar 

  21. Peña, D.: Análisis de Datos Multivariantes. Mc Graw Hill, New York (2002)

    Google Scholar 

  22. Praga-Alejo, R., Torres-Treviño, L., Piña-Monarrez, M.: Prediction in Welding Process Using Multiple Linear Regression and Neural Network. International Journal of Industrial Engineering, 481–488 (2008) ISSN 1072-4761

    Google Scholar 

  23. Praga-Alejo, R., Torres-Treviño, L., Piña-Monarrez, M.: Comparison between several neural networks models using statistical methods. In: Proceedings of the 13th Annual International Conference on Industrial Engineering Theory, Applications and Practice, Las Vegas, Nevada (September 7-10, 2008)

    Google Scholar 

  24. Rao, S., Nahm, A., Shi, Z., Deng, X., Syamil, A.: Artificial intelligence and expert systems applications in new product development. Kluwer Academic Publishers, USA (1999)

    Google Scholar 

  25. Sun, J., Xu, W., Liu, J.: Training RBF Neural Network via Quantum-Behaved Particle Swarm Optimization. In: King, I., Wang, J., Chan, L.-W., Wang, D. (eds.) ICONIP 2006. LNCS, vol. 4233, pp. 1156–1163. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  26. Tsoukalas, L., Uhrig, R.: Fuzzy and neural approaches in engineering. Wiley-Interscience Publication, USA (1998)

    Google Scholar 

  27. Valls, J.M., Aler, R., Fernández, O.: Using a Mahalanobis-Like Distance to train Radial Basis Neural Networks. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 257–263. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  28. Whitehead Bruce, A., Choate Timothy, D.: Cooperative – Competitive Genetic Evolution of Radial Basis Function Centers and Widths for Time Series Prediction. IEEE Transactions on Neural Networks (1995)

    Google Scholar 

  29. Whitehead Bruce, A.: Genetic Evolution of Radial Basis Function Coverage using Orthogonal Niches. IEEE Transactions on Neural Networks 7, 869–880 (1996)

    Article  Google Scholar 

  30. Yen, J., Langari, R.: Fuzzy Logic: Intelligence, Control and Information. Prenhall, USA (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Corporación Mexicana de Investigación en Materiales (COMIMSA), Calle Ciencia y Tecnología, No. 790, Frac. Saltillo 400, C.P. 25290, Saltillo, Coah, México

    Rolando J. Praga-Alejo, David S. González, Jorge Acevedo-Dávila & Francisco Cepeda

  2. Centro de Innovación, Investigación y Desarrollo en Ingeniería y Tecnología (CIIDIT), Km. 10 nueva carretera al Aeropuerto Internacional de Monterrey, PIIT Monterrey, Apodaca, Nuevo León, México

    Luis M. Torres-Treviño

Authors
  1. Rolando J. Praga-Alejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luis M. Torres-Treviño
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David S. González
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jorge Acevedo-Dávila
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Francisco Cepeda
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, , Av. Juan Dios Batiz, s/n, Zacatenco, 07738, Mexico City, México

    Grigori Sidorov

  2. Centro de Investigación en Matemáticas (CIMAT), Area de Computación, Callejón de Jalisco s/n, Mineral de Valenciana, Guanajuato, 36240, Guanajuato, México

    Arturo Hernández Aguirre

  3. Ciencias Computacionales, Instituto Nacional de Astrofísica, Optica y Electrónica (INAOE), Luis Enrique Erro No. 1, Santa María Tonantzintla, 72840, Puebla, México

    Carlos Alberto Reyes García

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Praga-Alejo, R.J., Torres-Treviño, L.M., González, D.S., Acevedo-Dávila, J., Cepeda, F. (2010). A Radial Basis Function Redesigned for Predicting a Welding Process. In: Sidorov, G., Hernández Aguirre, A., Reyes García, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16773-7_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16772-0

  • Online ISBN: 978-3-642-16773-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation