A real-valued genetic algorithm to optimize the parameters of support vector machine for classification of multiple faults in NPP

• Autorzy: Amer F. Z. , El-Garhy A. M. , Awadalla M. H. , Rashad S. M. , Abdien A. K.
• Języki publikacji: EN

Abstrakty

EN

Two parameters, regularization parameter c, which determines the trade off cost between minimizing the training error and minimizing the complexity of the model and parameter sigma (σ) of the kernel function which defines the non-linear mapping from the input space to some high-dimensional feature space, which constructs a non-linear decision hyper surface in an input space, must be carefully predetermined in establishing an efficient support vector machine (SVM) model. Therefore, the purpose of this study is to develop a genetic-based SVM (GASVM) model that can automatically determine the optimal parameters, c and sigma, of SVM with the highest predictive accuracy and generalization ability simultaneously. The GASVM scheme is applied on observed monitored data of a pressurized water reactor nuclear power plant (PWRNPP) to classify its associated faults. Compared to the standard SVM model, simulation of GASVM indicates its superiority when applied on the dataset with unbalanced classes. GASVM scheme can gain higher classification with accurate and faster learning speed.

Słowa kluczowe

EN

support vector machine (SVM); fault classification; multi fault classification; genetic algorithm (GA); machine learning

• Wydawca: Institute of Nuclear Chemistry and Technology
• Czasopismo: Nukleonika
• Rocznik: 2011
• Tom: Vol. 56, No. 4
• Strony: 323--332
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Amer F. Z.

autor

El-Garhy A. M.

autor

Awadalla M. H.

autor

Rashad S. M.

autor

Abdien A. K.

• Faculty of Engineering, Department of Electronics, Communications and Computers, Helwan University, Helwan Governerate, Helwan, Egypt,

Bibliografia

• 1. Avci E (2009) Selecting of the optimal feature subset and kernel parameters in digital modulation classification by using hybrid genetic algorithm – support vector machines:HGASVM. Expert Syst Appl 36:1391–1402
• 2. Babaoglu I, Findik O, Ulker E (2010) A comparison of feature selection models utilizing binary particle swarm optimization and genetic algorithm in determining coronary artery disease using support vector machine. Expert Syst Appl 37:3177–3183
• 3. Chen LH, Hsiao HD (2008) Feature selection to diagnose a business crisis by using a real GA-based support vector machine: an empirical study. Expert Syst Appl 35:1145–1155
• 4. Chou PH, Wu MJ, Chen KK (2010) Integrating support vector machine and genetic algorithm to implement dynamic wafer quality prediction system. Expert Syst Appl 37:4413–4424
• 5. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge University Press, Cambridge
• 6. Drucker H, Wu D, Joksons DW (1999) Support vector machine for spam categorization. IEEE Trans Neural Networks 10:1048–1054
• 7. Fei SW, Liu CL, Miao YB (2009) Support vector machine with genetic algorithm for forecasting of key-gas ratios in oil-immersed transformer. Expert Syst Appl 36:6326–6331
• 8. Fei SW, Sun Y (2008) Forecasting dissolved gases content in power transformer oil based on support vector machine with genetic algorithm. Electr Power Syst Res 78:507–514
• 9. Fei SW, Zhang XB (2009) Fault diagnosis of power transformer based on support vector machine with genetic algorithm. Expert Syst Appl 36:11352–11357
• 10. Gao JF, Shi WG, Tan JX, Zhong FJ (2002) Support vector machines based approach for fault diagnosis of valves in reciprocating pumps. IEEE Can Conf Electr Comput Eng 3:1622–1627
• 11.Guo GD, Li SZ, Chan KL (2001) Support vector machine for face recognition. Image Vision Comput 19:631–638
• 12.Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Networks 13:415–425
• 13. Huang C, Wang C (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31;2:231–240
• 14. Huanga HL, Chang FL (2007) ESVM: Evolutionary support vector machine for automatic feature selection and classification of microarray data. BioSystems 90:516–528
• 15. Huerta EB, Duval B, Hao JK (2006) A hybrid GA/SVM approach for gene selection and classification of microarray data. Springer-Verlag, Berlin
• 16. Kecman V (2001) Learning and soft computing. Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, US
• 17. Lahiri SK, Ghanta KC (2008) Prediction of pressure drop of slurry flow in pipeline by hybrid support vector regression and genetic algorithm model. Chin J Chem Eng 16;6:841–848
• 18. Lorena AC, de Carvalho ACPLF (2008) Evolutionary tuning of SVM parameter values in multiclass problems. Neurocomputing 71:3326–3334
• 19. Madeira MM, Tokhi MO, Ruano MG (2000) Real-time implementation of a Doppler signal spectral estimator using sequential and parallel processing techniques. Microprocess Microsyst 24:153–167
• 20. Merler S, Jurman G (2006) Terminated ramp – support vector machines: a nonparametric data dependent kernel. Neural Networks 19:1597–1611
• 21. Pai PF (2006) System reliability forecasting by support vector machines with genetic algorithms. Math Comput Modell 43:262–274
• 22. Samanta B, Al-Balushi KR, Al-Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16:657–665
• 23. Samia MR (1979) Dynamic energy balance of developing power systems with special reference to nuclear units performance. PhD thesis, Faculty of Engineering, Cairo University, Egypt
• 24. Sebald DJ, Bucklew JA (2000) Support vector machine techniques for nonlinear equalization. IEEE Trans Signal Process 48:3217–3226
• 25. Vapnik VN (1995) The nature of statistical learning theory. Springer-Verlag, New York, pp 157–173
• 26. Wahed ME, Ibrahim WZ (2010) Neural network and genetic algorithms for optimizing the plate element of Egyptian research reactor problems. Nucl Eng Des 240:191–197
• 27. Widodo A, Kim EY, Son JD et al. (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Sig Process 21:2560–2574
• 28. Wu CH, Tzeng GH, Goo YJ, Fang WC (2007) A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Syst Appl 32:397–408
• 29. Wu KP, Wan SD (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42;5:710–717