Neural Computing and Applications

, Volume 28, Issue 8, pp 2017–2028 | Cite as

Interpolation neural network model of a manufactured wind turbine

  • José de Jesús RubioEmail author
Original Article


In this paper, an interpolation neural network is introduced for the learning of a wind turbine behavior with incomplete data. The proposed hybrid method is the combination of an interpolation algorithm and a neural network. The interpolation algorithm is applied to estimate the missing data of all the variables; later, the neural network is employed to learn the output behavior. The proposed method avoids the requirement to know all the system data. Experiments show the effectiveness of the proposed technique.


Neural networks Interpolation Hybrid techniques  Wind turbine Incomplete data 



The author is grateful with the editor and with the reviewers for their valuable comments and insightful suggestions, which can help to improve this research significantly. The author thanks the Secretaría de Investigación y Posgrado, Comisión de Operación y Fomento de Actividades Académicas, and Consejo Nacional de Ciencia y Tecnología for their help in this research.


  1. 1.
    Bordignon F, Gomide F (2014) Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127:13–20CrossRefGoogle Scholar
  2. 2.
    Bouchachia A (2005) Learning with hybrid data. In: Proceedings of the fifth international conference on hybrid intelligent systems, pp 1–6Google Scholar
  3. 3.
    Bouchachia A (2010) An evolving classification cascade with self-learning. Evol Syst 1(3):143–160CrossRefGoogle Scholar
  4. 4.
    Cernuda C, Lughofer E, Hintenaus P, Marzinger W, Reischer T, Pawliczek M, Kasberger J (2013) Hybrid adaptive calibration methods and ensemble strategy for prediction of cloud point in melamine resin production. Chemometr Intell Lab Syst 126:60–75CrossRefGoogle Scholar
  5. 5.
    Chawla NV, Bowyer KW, Hall LO, Kegelmayr WP (2002) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357zbMATHGoogle Scholar
  6. 6.
    Cruz-Vega I, Yu W (2010) Multiple fuzzy neural networks modeling with sparse data. Neurocomputing 73:2446–2453CrossRefGoogle Scholar
  7. 7.
    Duviella E, Serir L, Sayed-Mouchaweh M (2013) An evolving classification approach for fault diagnosis and prognosis of a wind farm, conference on control and fault-tolerant systems (SysTol), pp 377–382Google Scholar
  8. 8.
    Elad M (2012) Sparse and redundant representation modeling-what next? IEEE Signal Process Lett 19(12):922–928CrossRefGoogle Scholar
  9. 9.
    Hartert L, Sayed-Mouchaweh M (2014) Dynamic supervised classification method for online monitoring in non-stationary environments. Neurocomputing 126:118–131CrossRefGoogle Scholar
  10. 10.
    Iglesias JA, Tiemblo A, Ledezma A, Sanchis A (2015) Web news mining in an evolving framework. Information fusion. doi: 10.1016/j.inffus.2015.07.004
  11. 11.
    Kazienko P, Lughofer E, Trawinski B (2013) Hybrid and ensemble methods in machine learning J.UCS special issue. J Univers Comput Sci 19(4):457–461Google Scholar
  12. 12.
    Leite D, Costa P, Gomide F (2013) Evolving granular neural networks from fuzzy data streams. Neural Netw 38:1–16CrossRefzbMATHGoogle Scholar
  13. 13.
    Lemos A, Caminhas W, Gomide F (2013) Adaptive fault detection and diagnosis using an evolving fuzzy classifier. Inf Sci 220:64–85CrossRefGoogle Scholar
  14. 14.
    Lughofer E (2012) Hybrid active learning for reducing the annotation effort of operators in classification systems. Pattern Recognit 45:884–896CrossRefGoogle Scholar
  15. 15.
    Lughofer E, Weigl E, Heidl W, Eitzinger C, Radauer T (2015) Integrating new classes on the fly in evolving fuzzy classifier designs and its application in visual inspection. Appl Soft Comput 35:558–582CrossRefGoogle Scholar
  16. 16.
    Maciel L, Gomide F, Ballini R (2014) Enhanced evolving participatory learning fuzzy modeling: an application for asset returns volatility forecasting. Evol Syst 5:75–88CrossRefGoogle Scholar
  17. 17.
    Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14(B):194–209CrossRefGoogle Scholar
  18. 18.
    Nuñez A, De Schutter B, Saez D, Skrjanc I (2014) Hybrid-fuzzy modeling and identification. Appl Soft Comput 17:67–78CrossRefGoogle Scholar
  19. 19.
    Ordoñez FJ, Iglesias JA, de Toledo P, Ledezma A, Sanchis A (2013) Online activity recognition using evolving classifiers. Expert Syst Appl 40:1248–1255CrossRefGoogle Scholar
  20. 20.
    Pratama M, Anavatti SG, Angelov PP, Lughofer E (2014) PANFIS: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55–68CrossRefGoogle Scholar
  21. 21.
    Pratama M, Er MJ, Li X, Oentaryo RJ, Lughofer E, Arifin I (2013) Data driven modeling based on dynamic parsimonious fuzzy neural network. Neurocomputing 110:18–28CrossRefGoogle Scholar
  22. 22.
    Pratama M, Anavatti SG, Lughofer E (2014) GENEFIS: toward an effective localist network. IEEE Trans Fuzzy Syst 22(3):547–562CrossRefGoogle Scholar
  23. 23.
    Pratama M, Anavatti SG, Er MJ, Lughofer ED (2015) pClass: an effective classifier for streaming examples. IEEE Trans Fuzzy Syst 23(2):369–386CrossRefGoogle Scholar
  24. 24.
    Pratama M, Anavatti SG, Lu J (2015) Recurrent classifier based on an incremental meta-cognitive-based scaffolding algorithm. IEEE Trans Fuzzy Syst. doi: 10.1109/TFUZZ.2015.2402683
  25. 25.
    Pratama M, Lu J, Zhang G (2015) Evolving type-2 fuzzy classifier. IEEE Trans Fuzzy Syst. doi: 10.1109/TFUZZ.2015.2463732
  26. 26.
    Rosa R, Gomide F, Ballini R (2013) Evolving hybrid neural fuzzy network for system modeling and time series forecasting, 12th international conference on machine learning and applications, pp 1–6Google Scholar
  27. 27.
    Rubio JJ, Angelov P, Pacheco J (2011) An uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw 22(3):356–366CrossRefGoogle Scholar
  28. 28.
    Rubio JJ (2014) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14(B):259–268CrossRefGoogle Scholar
  29. 29.
    Rubio JJ (2015) Fuzzy slopes model of nonlinear systems with sparse data. Soft Comput. doi: 10.1007/s00500-014-1289-6
  30. 30.
    Rubio JJ, Vazquez DM, Mujica-Vargas D (2013) Acquisition system and approximation of brain signals. IET Sci Meas Technol 7(4):232–239CrossRefGoogle Scholar
  31. 31.
    Rubio JJ, Soriano LA, Yu W (2014) Dynamic model of a wind turbine for the electric energy generation. Math Probl Eng 2014:1–8Google Scholar
  32. 32.
    Tao L, Elhamifar E, Khudanpur S, Hager GD, Vidal R (1012) Sparse hidden markov models for surgical gesture classification and skill evaluation. Lecture notes in artificial intelligence, pp 167–177Google Scholar
  33. 33.
    Toubakh H, Sayed-mouchaweh M, Duviella E (2013) Advanced pattern recognition approach for fault diagnosis of wind turbines. 12th international conference on machine learning and applications, pp 368–373Google Scholar
  34. 34.
    Wang LX (1997) A course in fuzzy systems and control. ISBN: 0-13-540882-2Google Scholar
  35. 35.
    Zhang S, Zhan Y, Dewan M, Huang J, Metaxas DN, Zhou XS (2012) Towards robust and effective shape modeling: sparse shape composition. Med Image Anal 16:265–277CrossRefGoogle Scholar
  36. 36.
    Zhong LW, Kwok JT (2012) Efficient sparse modeling with automatic feature grouping. IEEE Trans Neural Netw Learn Syst 23(9):1436–1447CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  1. 1.Sección de Estudios de Posgrado e Investigación, ESIME AzcapotzalcoInstituto Politécnico NacionalMexicoMexico

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