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.
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References
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)
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: 697-7912
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)
Bouchachia, A.: Radial Basis Function Nets for Time Series Prediction. International Journal of Computation Intelligence Systems (2), 147–157 (2009)
Shengli1, Z., Yan, L.: Performance Prediction of Commercial Concrete Based on RBF Neural Network. Journal of Changsha University of Electric Power (Natural Science) (2001)
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)
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)
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© 2011 Springer-Verlag Berlin Heidelberg
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González, A.G., Molina, J.C.M., Bernal, P.J.A., Ayala, F.J.Z. (2011). A Predictive Control System for Concrete Plants. Application of RBF Neural Networks for Reduce Dosing Inaccuracies. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślȩzak, D. (eds) Soft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011. Advances in Intelligent and Soft Computing, vol 87. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19644-7_15
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DOI: https://doi.org/10.1007/978-3-642-19644-7_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19643-0
Online ISBN: 978-3-642-19644-7
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