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Volumn 30, Issue , 2015, Pages 792-802

System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection

Author keywords

Analytic selection; Genetic algorithms; Parameter selection; Reliability prediction; Support vector regression; Time series forecasting

Indexed keywords

ALGORITHMS; FORECASTING; PARAMETER ESTIMATION; REGRESSION ANALYSIS; RELIABILITY; TIME SERIES;

EID: 84924530099     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.02.026     Document Type: Article
Times cited : (52)

References (41)
  • 1
    • 84879322256 scopus 로고    scopus 로고
    • A dynamic particle filter-support vector regression method for reliability prediction
    • Z. Wei, T. Tao, and D. ZhuoShu A dynamic particle filter-support vector regression method for reliability prediction Reliab. Eng. Syst. Saf. 119 2013 109 116
    • (2013) Reliab. Eng. Syst. Saf. , vol.119 , pp. 109-116
    • Wei, Z.1    Tao, T.2    Zhuoshu, D.3
  • 2
    • 84861874826 scopus 로고    scopus 로고
    • Overview of reliability testing
    • E.A. Elsayed Overview of reliability testing IEEE Trans. Reliab. 61 2012 282 291
    • (2012) IEEE Trans. Reliab. , vol.61 , pp. 282-291
    • Elsayed, E.A.1
  • 3
    • 0041857534 scopus 로고
    • Non-homogeneous autoregressive processes for tracking (software) reliability growth, and their Bayesian analysis
    • N.D. Singpurwalla, and R. Soyer Non-homogeneous autoregressive processes for tracking (software) reliability growth, and their Bayesian analysis J. R. Stat. Soc. Ser. B (Methodol.) 1992 145 156
    • (1992) J. R. Stat. Soc. Ser. B (Methodol.) , pp. 145-156
    • Singpurwalla, N.D.1    Soyer, R.2
  • 4
    • 84868624158 scopus 로고    scopus 로고
    • An empirical study of software reliability prediction using machine learning techniques
    • P. Kumar, and Y. Singh An empirical study of software reliability prediction using machine learning techniques Int. J. Syst. Assur. Eng. Manage. 3 2012 194 208
    • (2012) Int. J. Syst. Assur. Eng. Manage. , vol.3 , pp. 194-208
    • Kumar, P.1    Singh, Y.2
  • 5
    • 0033079441 scopus 로고    scopus 로고
    • Evaluation of power systems reliability by an artificial neural network
    • N. Amjady, and M. Ehsan Evaluation of power systems reliability by an artificial neural network IEEE Trans. Power Syst. 14 1999 287 292
    • (1999) IEEE Trans. Power Syst. , vol.14 , pp. 287-292
    • Amjady, N.1    Ehsan, M.2
  • 6
    • 0037061422 scopus 로고    scopus 로고
    • A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction
    • S. Ho, M. Xie, and T. Goh A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction Comp. Ind. Eng. 42 2002 371 375
    • (2002) Comp. Ind. Eng. , vol.42 , pp. 371-375
    • Ho, S.1    Xie, M.2    Goh, T.3
  • 7
    • 84947662956 scopus 로고
    • Using neural networks in reliability prediction
    • N. Karunanithi, D. Whitley, and Y.K. Malaiya Using neural networks in reliability prediction IEEE Softw. 9 1992 53 59
    • (1992) IEEE Softw. , vol.9 , pp. 53-59
    • Karunanithi, N.1    Whitley, D.2    Malaiya, Y.K.3
  • 8
    • 67349288151 scopus 로고    scopus 로고
    • Fault diagnosis of power transformer based on support vector machine with genetic algorithm
    • S.-W. Fei, and X.-B. Zhang Fault diagnosis of power transformer based on support vector machine with genetic algorithm Expert Syst. Appl. 36 2009 11352 11357
    • (2009) Expert Syst. Appl. , vol.36 , pp. 11352-11357
    • Fei, S.-W.1    Zhang, X.-B.2
  • 10
    • 27144489164 scopus 로고    scopus 로고
    • A tutorial on support vector machines for pattern recognition
    • C.C. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 1998 121 167
    • (1998) Data Min. Knowl. Discov. , vol.2 , pp. 121-167
    • Burges, C.C.1
  • 11
    • 4043137356 scopus 로고    scopus 로고
    • A tutorial on support vector regression
    • A. Smola, and B. Schölkopf A tutorial on support vector regression Statist. Comput. 14 2004 199 222
    • (2004) Statist. Comput. , vol.14 , pp. 199-222
    • Smola, A.1    Schölkopf, B.2
  • 12
    • 80855131458 scopus 로고    scopus 로고
    • Application of support vector regression to genome-assisted prediction of quantitative traits
    • N. Long, D. Gianola, and G.J.M. Rosa Application of support vector regression to genome-assisted prediction of quantitative traits Theor. Appl. Genet. 123 2011 1065 1074
    • (2011) Theor. Appl. Genet. , vol.123 , pp. 1065-1074
    • Long, N.1    Gianola, D.2    Rosa, G.J.M.3
  • 13
    • 41049086704 scopus 로고    scopus 로고
    • A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression
    • S. Van Looy, T. Verplancke, and D. Benoit A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression Crit. Care 2007 11
    • (2007) Crit. Care , pp. 11
    • Van Looy, S.1    Verplancke, T.2    Benoit, D.3
  • 14
    • 84890101768 scopus 로고    scopus 로고
    • Reliability prediction through guided tail modeling using support vector machines Proceedings of the Institution of Mechanical Engineers
    • December
    • E. Acar Reliability prediction through guided tail modeling using support vector machines Proceedings of the Institution of Mechanical Engineers Part C: Journal of Mechanical Engineering 227 2013 2780 2794 December
    • (2013) Part C: Journal of Mechanical Engineering , vol.227 , pp. 2780-2794
    • Acar, E.1
  • 15
    • 33644676683 scopus 로고    scopus 로고
    • Predicting engine reliability by support vector machines
    • W.C. Hong, and P.F. Pai Predicting engine reliability by support vector machines Int. J. Adv. Manuf. Technol. 28 2006 154 161
    • (2006) Int. J. Adv. Manuf. Technol. , vol.28 , pp. 154-161
    • Hong, W.C.1    Pai, P.F.2
  • 16
    • 84879324237 scopus 로고    scopus 로고
    • Recurrent support vector machines in reliability prediction
    • L. Wang, K. Chen, Y. Ong, Springer Berlin, Heidelberg
    • W.C. Hong, P.F. Pai, and C.T. Chen Recurrent support vector machines in reliability prediction L. Wang, K. Chen, Y. Ong, Advances in Natural Computation 2005 Springer Berlin, Heidelberg 407
    • (2005) Advances in Natural Computation , pp. 407
    • Hong, W.C.1    Pai, P.F.2    Chen, C.T.3
  • 17
    • 80052449651 scopus 로고    scopus 로고
    • Failure and reliability prediction by support vector machines regression of time series data
    • M.C. Moura, E. Zio, and I. Didier Lins Failure and reliability prediction by support vector machines regression of time series data Reliab. Eng. Syst. Saf. 96 2011 1527 1534
    • (2011) Reliab. Eng. Syst. Saf. , vol.96 , pp. 1527-1534
    • Moura, M.C.1    Zio, E.2    Didier Lins, I.3
  • 18
    • 84879305799 scopus 로고    scopus 로고
    • Application research of support vector machines in condition trend prediction of mechanical equipment
    • J. Yang, and Y. Zhang Application research of support vector machines in condition trend prediction of mechanical equipment Adv. Neural Netw. - ISNN 2005 971 2005
    • (2005) Adv. Neural Netw. - ISNN , pp. 971-2005
    • Yang, J.1    Zhang, Y.2
  • 21
    • 80255133268 scopus 로고    scopus 로고
    • A new approach for time series prediction using ensembles of ANFIS models
    • P. Melin, J. Soto, and O. Castillo A new approach for time series prediction using ensembles of ANFIS models Expert Syst. Appl. 39 2012 3494 3506
    • (2012) Expert Syst. Appl. , vol.39 , pp. 3494-3506
    • Melin, P.1    Soto, J.2    Castillo, O.3
  • 23
    • 0346250790 scopus 로고    scopus 로고
    • Practical selection of SVM parameters and noise estimation for SVM regression
    • V. Cherkassky, and Y. Ma Practical selection of SVM parameters and noise estimation for SVM regression Neural Netw. 17 2004 113 126
    • (2004) Neural Netw. , vol.17 , pp. 113-126
    • Cherkassky, V.1    Ma, Y.2
  • 26
    • 51249185617 scopus 로고
    • The ellipsoid method and its consequences in combinatorial optimization
    • M. Grötschel, L. Lovász, and A. Schrijver The ellipsoid method and its consequences in combinatorial optimization Combinatorica 1 1981 169 197
    • (1981) Combinatorica , vol.1 , pp. 169-197
    • Grötschel, M.1    Lovász, L.2    Schrijver, A.3
  • 27
    • 0032117046 scopus 로고    scopus 로고
    • Implementation of the simultaneous perturbation algorithm for stochastic optimization
    • J.C. Spall Implementation of the simultaneous perturbation algorithm for stochastic optimization IEEE Trans. Aerosp. Electron. Syst. 34 1998 817 823
    • (1998) IEEE Trans. Aerosp. Electron. Syst. , vol.34 , pp. 817-823
    • Spall, J.C.1
  • 28
    • 33646130599 scopus 로고    scopus 로고
    • Software reliability forecasting by support vector machines with simulated annealing algorithms
    • P.F. Pai, and W.C. Hong Software reliability forecasting by support vector machines with simulated annealing algorithms J. Syst. Softw. 79 2006 747 755
    • (2006) J. Syst. Softw. , vol.79 , pp. 747-755
    • Pai, P.F.1    Hong, W.C.2
  • 29
    • 33845633182 scopus 로고    scopus 로고
    • Forecasting systems reliability based on support vector regression with genetic algorithms
    • K.Y. Chen Forecasting systems reliability based on support vector regression with genetic algorithms Reliab. Eng. Syst. Saf. 92 2007 423 432
    • (2007) Reliab. Eng. Syst. Saf. , vol.92 , pp. 423-432
    • Chen, K.Y.1
  • 30
    • 78751627947 scopus 로고    scopus 로고
    • SVR with hybrid chaotic genetic algorithms for tourism demand forecasting
    • W.-C. Hong, Y. Dong, and L.-Y. Chen SVR with hybrid chaotic genetic algorithms for tourism demand forecasting Appl. Soft Comput. 11 2011 1881 1890
    • (2011) Appl. Soft Comput. , vol.11 , pp. 1881-1890
    • Hong, W.-C.1    Dong, Y.2    Chen, L.-Y.3
  • 31
    • 48749109333 scopus 로고    scopus 로고
    • Particle swarm optimization for parameter determination and feature selection of support vector machines
    • S.W. Lin, K.C. Ying, and S.C. Chen Particle swarm optimization for parameter determination and feature selection of support vector machines Expert Syst. Appl. 35 2008 1817 1824
    • (2008) Expert Syst. Appl. , vol.35 , pp. 1817-1824
    • Lin, S.W.1    Ying, K.C.2    Chen, S.C.3
  • 32
    • 84857637199 scopus 로고    scopus 로고
    • A particle swarm-optimized support vector machine for reliability prediction
    • I.D. Lins, M.C. Moura, and E. Zio A particle swarm-optimized support vector machine for reliability prediction Qual. Reliab. Eng. Int. 28 2012 141 158
    • (2012) Qual. Reliab. Eng. Int. , vol.28 , pp. 141-158
    • Lins, I.D.1    Moura, M.C.2    Zio, E.3
  • 33
    • 84874649268 scopus 로고    scopus 로고
    • Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic
    • P. Melin, F. Olivas, and O. Castillo Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic Expert Syst. Appl. 40 2013 3196 3206
    • (2013) Expert Syst. Appl. , vol.40 , pp. 3196-3206
    • Melin, P.1    Olivas, F.2    Castillo, O.3
  • 35
    • 0002338687 scopus 로고
    • A genetic algorithm tutorial
    • D. Whitley A genetic algorithm tutorial Stat. Comput. 4 1994 65 85
    • (1994) Stat. Comput. , vol.4 , pp. 65-85
    • Whitley, D.1
  • 38
    • 84958987386 scopus 로고    scopus 로고
    • Linear dependency between ε and the input noise in ε-support vector regression
    • G. Dorffner, H. Bischof, K. Hornik, Springer Berlin, Heidelberg
    • J. Kwok Linear dependency between ε and the input noise in ε-support vector regression G. Dorffner, H. Bischof, K. Hornik, Artificial Neural Networks - ICANN 2001 2001 Springer Berlin, Heidelberg 405 410
    • (2001) Artificial Neural Networks - ICANN 2001 , pp. 405-410
    • Kwok, J.1
  • 40
    • 15944420505 scopus 로고    scopus 로고
    • Application of neural networks in forecasting engine systems reliability
    • K. Xu, M. Xie, and L.C. Tang Application of neural networks in forecasting engine systems reliability Appl. Soft Comput. 2 2003 255 268
    • (2003) Appl. Soft Comput. , vol.2 , pp. 255-268
    • Xu, K.1    Xie, M.2    Tang, L.C.3
  • 41
    • 71249096917 scopus 로고    scopus 로고
    • Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making
    • 20-24 August
    • F. Valdez, P. Melin, and O. Castillo Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making FUZZ-IEEE 2009 IEEE International Conference on Fuzzy Systems 20-24 August 2009 2114 2119
    • (2009) FUZZ-IEEE 2009 IEEE International Conference on Fuzzy Systems , pp. 2114-2119
    • Valdez, F.1    Melin, P.2    Castillo, O.3


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