Intelligent Model to Obtain Current Extinction Angle for a Single Phase Half Wave Controlled Rectifier with Resistive and Inductive Load

  • José Luis Calvo-Rolle
  • Héctor Quintián
  • Emilio Corchado
  • Ramón Ferreiro-García
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 188)

Abstract

The present work show the model of regression based on intelligent methods. It has been created to obtain current extinction angle for a half wave controlled rectifier. The system is a typically non-linear case of study that requires a hard work to solve it manually. First, all the work points are calculated for the operation range. Then with the dataset, to achieve the final solution, several methods of regression have been tested from traditional to intelligent types. The model is verified empirically with electronic circuit software simulation and analytical methods. The model allows obtaining good results in all the operating range.

Keywords

Half wave controlled rectifier regression non-linear model MLP SVM polynomial models LWP K-NN 

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References

  1. 1.
    Malvino, A.P., Bates, D.J.: Electronic principles. Recording for Blind & Dyslexic, Princeton (2008)Google Scholar
  2. 2.
    Hart, D.W.: Power Electronics. McGraw-Hill, New York (2011)Google Scholar
  3. 3.
    Rashid, M.H.: Power electronics handbook: devices, circuits, and applications. Butterworth-Heinemann, Burlington (2011)Google Scholar
  4. 4.
    Mohan, N.: Power electronics: a first course, Hoboken, N.J. (2012)Google Scholar
  5. 5.
    Coleman, T.F., Li, Y.: An Interior, Trust Region Approach for Nonlinear Minimization Subject to Bounds. SIAM Journal on Optimization 6, 418–445 (1996)MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    Coleman, T.F., Li, Y.: On the Convergence of Reflective Newton Methods for Large-Scale Nonlinear Minimization Subject to Bounds. Mathematical Programming 67(2), 189–224 (1994)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Levenberg, K.: A Method for the Solution of Certain Problems in Least-Squares. Quarterly Applied Mathematics 2, 164–168 (1944)MathSciNetMATHGoogle Scholar
  8. 8.
    Marquardt, D.: An Algorithm for Least-squares Estimation of Nonlinear Parameters. SIAM Journal Applied Mathematics 11, 431–441 (1963)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Moré, J.J.: The Levenberg-Marquardt Algorithm: Implementation and Theory. In: Watson, G.A. (ed.) Numerical Analysis. Lecture Notes in Mathematics, vol. 630, pp. 105–116. Springer (1977)Google Scholar
  10. 10.
    Mark, J., Goldberg, M.: Multiple Regression Analysis and Mass Assessment: A Review of the Issues. Appraisal Journal 56(1), 89–109 (1988)Google Scholar
  11. 11.
    Do, A.Q., Grudnitski, G.: A Neural Network Approach to Residential Property Appraisal. The Real Estate Appraiser 58(3), 38–45 (1992)Google Scholar
  12. 12.
    Larsen, J.E., Peterson, M.O.: Correcting for Errors in Statistical Appraisal Equations. The Real Estate Appraiser and Analyst 54(3), 45–49 (1988)Google Scholar
  13. 13.
    Limsombunchai, V., Gan, C., Lee, M.: House Price Prediction: Hedonic Price Model Vs. Artificial Neural Network. American Journal of Applied Sciences 1(3), 193–201 (2004)Google Scholar
  14. 14.
    Worzala, E., Lenk, M., Silva, A.: An Exploration of Neural Networks and Its Application to Real Estate Valuation. Journal of Real Estate Research 10, 185–202 (1995)Google Scholar
  15. 15.
    Guan, J., Levitan, A.S.: Artificial Neural Network Based Assessment of Residential Real Estate Property Prices: A Case Study. Accounting Forum 20(3/4), 311–326 (1997)Google Scholar
  16. 16.
    Taffese, W.Z.: Case-Based Reasoning and Neural Networks for Real Estate Valuation. In: Proceedings of 25th International Multi-Conference: Artificial Intelligence and Applications, Innsbruck, Austria, pp. 84–89 (2007)Google Scholar
  17. 17.
    Guan, J., Zurada, J., Levitan, A.S.: An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment. Journal of Real Estate Research 30(4), 395–420 (2008)Google Scholar
  18. 18.
    Peterson, S., Flanagan, A.B.: Neural Network Hedonic Pricing Models in Mass Real Estate Appraisal. Journal of Real Estate Research 31(2), 147–164 (2009)Google Scholar
  19. 19.
    Bishop, C.M.: Pattern recognition and machine learning. Springer, New York (2006)MATHGoogle Scholar
  20. 20.
    Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge (2000)Google Scholar
  21. 21.
    Ye, J., Xiong, T.: Svm versus least squares svm. In: The 11th International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 640–647 (2007)Google Scholar
  22. 22.
    Yankun, L., Xueguang, S., Wensheng, C.: A consensus least support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples. Talanta 72, 217–222 (2007)CrossRefGoogle Scholar
  23. 23.
    De Brabanter, K., Karsmakers, P., Ojeda, F., Alzate, C., De Brabanter, J., Pelckmans, K., De Moor, B., Vandewalle, J., Suykens, J.A.K.: LS-SVMlab Toolbox User’s Guide version 1.7 (2010), http://www.esat.kuleuven.be/sista/lssvmlab/
  24. 24.
    Xavier de Souza, S., Suykens, J.A.K., Vandewalle, J., Bolle, D.: Coupled Simulated Annealing. IEEE Transactions on Systems, Man and Cybernetics - Part B 40(2), 320–335 (2010)CrossRefGoogle Scholar
  25. 25.
    Cleveland, W.S., Devlin, S.J.: Locally weighted regression: An approach to regression analysis by local fitting. Journal of the American Statistical Association 83, 596–610 (1988)MATHCrossRefGoogle Scholar
  26. 26.
    Duda, R.O., Hart, P.E., Strork, D.G.: Pattern Classification, 2nd edn. Wiley, Chichester (2001)MATHGoogle Scholar
  27. 27.
    David, J., Henao, V.: Neuroscheme: A modeling language for artificial neural networks. Dyna-Colombia 147, 75–82 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • José Luis Calvo-Rolle
    • 1
  • Héctor Quintián
    • 2
  • Emilio Corchado
    • 2
  • Ramón Ferreiro-García
    • 1
  1. 1.Department of Industrial EngineeringUniversity of CoruñaFerrol, A CoruñaSpain
  2. 2.Departmento de Informática y AutomáticaUniversity of SalamancaSalamancaSpain

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