Advertisement

A Predictive Control System for Concrete Plants. Application of RBF Neural Networks for Reduce Dosing Inaccuracies

  • Antonio Guerrero González
  • Juan Carlos Molina Molina
  • Pedro José Ayala Bernal
  • Francisco José Zamora Ayala
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 87)

Abstract

In industry, a comprehensive control process is necessary in order to ensure the quality of a manufactured product. In the manufacturing process of concrete, the variables are dependent on several factors, some of them external, which require very precise estimation. To resolve this problem we use techniques based on artificial neural networks. Throughout this paper we describe an RBF (Radial Basis Function) neural network, designed and trained for the prediction of radial in concrete manufacturing plants. With this predictive algorithm we have achieved results that have significantly improved upon those obtained to date using other methods in the concrete industry.

Keywords

RBF Prediction Neural Network Concrete Dosing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Reinhart, G., Gartner, J.: Reduction of Systematic Dosing Inaccuracies During the Application of Highly Viscous Substances. CIRP Annals – Manufacturing Technology 50(1), 1–4 (2001)CrossRefGoogle Scholar
  2. 2.
    Valverde Gil, R., Gachet Páez, D.: Identificación de Sistemas Dinámicos Utilizando Redes Neuronales RBF. Revista Iberoamericana de Automática e Informática Industrial 4(2), 32–42 (2007), ISSN: 697-7912Google Scholar
  3. 3.
    Li, Y., Sundararajan, N., Saratchadran, P.: Analysis of Minimal Radial Basis Function in Network Algorithm for Real-Time Identification of Nonlinear Dynamic Systems. IEE Proc. On Control Theory and Applications 147(4), 476–484 (2000)CrossRefGoogle Scholar
  4. 4.
    Bouchachia, A.: Radial Basis Function Nets for Time Series Prediction. International Journal of Computation Intelligence Systems (2), 147–157 (2009)CrossRefGoogle Scholar
  5. 5.
    Shengli1, Z., Yan, L.: Performance Prediction of Commercial Concrete Based on RBF Neural Network. Journal of Changsha University of Electric Power (Natural Science) (2001)Google Scholar
  6. 6.
    Nataraj, M.C., Ravikumar, C.N., Jayaram, M.A.: An Integrated Soft Computing Technique for Proportioning Standard Concrete Mixes. New Building Materials and Construction World 11(7) (2006)Google Scholar
  7. 7.
    Yeh, I.-C.: Analysis of Strength of Concrete Using Design of Experiments and Neural Networks. Journal of Materials in Civil Engineering, ASCE, 597–604 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Guerrero González
    • 1
  • Juan Carlos Molina Molina
    • 1
  • Pedro José Ayala Bernal
    • 2
  • Francisco José Zamora Ayala
    • 2
  1. 1.Dpto. de Ingeniería de Sistemas y AutomáticaUniversidad Politécnica de CartagenaCartagenaSpain
  2. 2.Dpto. de Automatización de Frumecar S.L.MurciaSpain

Personalised recommendations