Abstract
The clean energy use has increased during the last years, especially, electricity generation through wind energy. Wind generator blades are usually made by bicomponent mixing machines. With the aim to predict the behavior of this type of manufacturing systems, it has been developed a model that allows to know the performance of a real bicomponent mixing equipment. The novel approach has been obtained by using clustering combined with regression techniques with a dataset obtained during the system operation. Finally, the created model has been tested with very satisfactory results.
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Alvarez-Huerta, A., Gonzalez-Miguelez, R., García-Metola, D., Noriega-Gonzalez, A.: Drywell tempeture prediction of a nuclear power plant by means of artificial neural networks. Dyna 86(4), 467–473 (2011)
Bishop, C.: Pattern recognition and machine learning (information science and statistics). Springer-Verlag New York, Inc., Secaucus (2006)
Calvo-Rolle, J., Casteleiro-Roca, J., Quintián, H., Meizoso-Lopez, M.: A hybrid intelligent system for PID controller using in a steel rolling process. Expert Systems with Applications 40(13), 5188–5196 (2013)
Cherif, A., Cardot, H., Boné, R.: SOM time series clustering and prediction with recurrent neural networks. Neurocomput. 74(11), 1936–1944 (2011)
Cristianini, N., Shawe-Taylor, J.: An introduction to support Vector Machines and other kernel-based learning methods. Cambridge University Press, New York (2000)
Fan, H., Wong, C., Yuen, M.F.: Prediction of material properties of epoxy materials using molecular dynamic simulation. In: 7th International Conference on Thermal, Mechanical and Multiphysics Simulation and Experiments in Micro-Electronics and Micro-Systems, EuroSime 2006, pp. 1–4 (April 2006)
Garg, L., Mcclean, S., Meenan, B., Millard, P.: Phase-type survival trees and mixed distribution survival trees for clustering patients’ hospital length of stay. Informatica 22(1), 57–72 (2011)
Ghaseminezhad, M.H., Karami, A.: A novel self-organizing map (SOM) neural network for discrete groups of data clustering. Appl. Soft Comput. 11(4), 3771–3778 (2011)
Guo, Y., Li, X., Bai, G., Ma, J.: Time series prediction method based on LS-SVR with modified gaussian RBF. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds.) ICONIP 2012, Part II. LNCS, vol. 7664, pp. 9–17. Springer, Heidelberg (2012)
Jacobs, R., Jordan, M., Nowlan, S., Hinton, G.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79–87 (1991)
Jordan, M., Jacobs, R.: Hierarchical mixtures of experts and the EM algorithm. Neural Comput. 6(2), 181–214 (1994)
Karasuyama, M., Nakano, R.: Optimizing svr hyperparameters via fast cross-validation using aosvr. In: International Joint Conference on Neural Networks, IJCNN 2007, pp. 1186–1191 (August 2007)
Kaski, S., Sinkkonen, J., Klami, A.: Discriminative clustering. Neurocomputing 69(13), 18–41 (2005)
Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78(9), 1464–1480 (1990)
Kohonen, T.: Exploration of very large databases by self-organizing maps. In: International Conference on Neural Networks, vol. 1, pp. PL1–PL6 (1997)
Kohonen, T., Oja, E., Simula, O., Visa, A., Kangas, J.: Engineering applications of the self-organizing map. Proceedings of the IEEE 84(10), 1358–1384 (1996)
Li, H., Chen, Z.: Overview of different wind generator systems and their comparisons. IET Renewable Power Generation 2(2), 123–138 (2008)
Martínez-Rego, D., Fontenla-Romero, O., Alonso-Betanzos, A.: Efficiency of local models ensembles for time series prediction. Expert Syst. Appl. 38(6), 6884–6894 (2011)
Pal, N., Biswas, J.: Cluster validation using graph theoretic concepts. Pattern Recognition 30(6), 847–857 (1997)
Qin, A., Suganthan, P.: Enhanced neural gas network for prototype-based clustering. Pattern Recogn. 38(8), 1275–1288 (2005)
Steinwart, I., Christmann, A.: Support vector machines, 1st edn. Springer Publishing Company, Incorporated (2008)
Suykens, J., Vandewalle, J.: Least squares support vector machine slassifiers. Neural Processing Letters 9(3), 293–300 (1999)
Šutienė, K., Makackas, D., Pranevičius, H.: Multistage k-means clustering for scenario tree construction. Informatica 21(1), 123–138 (2010)
Wang, L., Wu, J.: Neural network ensemble model using PPR and LS-SVR for stock market forecasting. In: Huang, D.-S., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2011. LNCS, vol. 6838, pp. 1–8. Springer, Heidelberg (2011)
Wang, R., Wang, A.-M., Song, Q.: Research on the alkalinity of sintering process based on LS-SVM algorithms. In: Jin, D., Lin, S. (eds.) Advances in CSIE, Vol. 1. AISC, vol. 168, pp. 449–454. Springer, Heidelberg (2012)
Wasserman, P.: Advanced methods in neural computing, 1st edn. John Wiley & Sons, Inc., New York (1993)
Ye, J., Xiong, T.: Svm versus least squares SVM. Journal of Machine Learning Research - Proceedings Track 2, 644–651 (2007)
Young, W.B., Wu, W.H.: Optimization of the skin thickness distribution in the composite wind turbine blade. In: 2011 International Conference on Fluid Power and Mechatronics (FPM), pp. 62–66 (August 2011)
Zeng, Z., Wang, J.: Advances in neural network research and applications, 1st edn. Springer Publishing Company, Incorporated (2010)
Zuo, Y., Liu, H.: Evaluation on comprehensive benefit of wind power generation and utilization of wind energy. In: 2012 IEEE 3rd International Conference on Software Engineering and Service Science (ICSESS), pp. 635–638 (June 2012)
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Jove, E., Aláiz-Moretón, H., Casteleiro-Roca, J.L., Corchado, E., Calvo-Rolle, J.L. (2014). Modeling of Bicomponent Mixing System Used in the Manufacture of Wind Generator Blades. In: Corchado, E., Lozano, J.A., Quintián, H., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2014. IDEAL 2014. Lecture Notes in Computer Science, vol 8669. Springer, Cham. https://doi.org/10.1007/978-3-319-10840-7_34
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DOI: https://doi.org/10.1007/978-3-319-10840-7_34
Publisher Name: Springer, Cham
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