System reliability prediction by support vector regression with analytic selection and genetic algorithm parameters selection
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[1] Oscar Castillo,et al. A new approach for time series prediction using ensembles of ANFIS models , 2012, Expert Syst. Appl..
[2] Shih-Wei Lin,et al. Particle swarm optimization for parameter determination and feature selection of support vector machines , 2008, Expert Syst. Appl..
[3] Kalyanmoy Deb,et al. Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..
[4] Kuan-Yu Chen,et al. Forecasting systems reliability based on support vector regression with genetic algorithms , 2007, Reliab. Eng. Syst. Saf..
[5] Yunqian Ma,et al. Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.
[6] Gwilym M. Jenkins,et al. Time series analysis, forecasting and control , 1971 .
[7] Enrico Zio,et al. A dynamic particle filter-support vector regression method for reliability prediction , 2013, Reliab. Eng. Syst. Saf..
[8] S. Gabel,et al. Using Neural Networks , 2003 .
[9] Enrico Zio,et al. Failure and reliability prediction by support vector machines regression of time series data , 2011, Reliab. Eng. Syst. Saf..
[10] Nozer D. Singpurwalla,et al. Non-homogeneous Autoregressive Processes for Tracking (Software) Reliability Growth, and their Bayesian Analysis , 1992 .
[11] Wei-Chiang Hong,et al. SVR with hybrid chaotic genetic algorithms for tourism demand forecasting , 2011, Appl. Soft Comput..
[12] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[13] Johan Decruyenaere,et al. A novel approach for prediction of tacrolimus blood concentration in liver transplantation patients in the intensive care unit through support vector regression , 2007, Critical care.
[14] Loon Ching Tang,et al. Application of neural networks in forecasting engine systems reliability , 2003, Appl. Soft Comput..
[15] Thong Ngee Goh,et al. A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction , 2002 .
[16] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[17] Martin Grötschel,et al. The ellipsoid method and its consequences in combinatorial optimization , 1981, Comb..
[18] L. Darrell Whitley,et al. Using neural networks in reliability prediction , 1992, IEEE Software.
[19] Ryohei Nakano,et al. Optimizing Support Vector regression hyperparameters based on cross-validation , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[20] Enrico Zio,et al. A particle swarm‐optimized support vector machine for reliability prediction , 2012, Qual. Reliab. Eng. Int..
[21] Daniel Gianola,et al. Application of support vector regression to genome-assisted prediction of quantitative traits , 2011, Theoretical and Applied Genetics.
[22] Ping-Feng Pai,et al. Predicting engine reliability by support vector machines , 2006 .
[23] Yogesh Singh,et al. An empirical study of software reliability prediction using machine learning techniques , 2012, Int. J. Syst. Assur. Eng. Manag..
[24] Oscar Castillo,et al. Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making , 2009, 2009 IEEE International Conference on Fuzzy Systems.
[25] Mehdi Ehsan,et al. Evaluation of power systems reliability by an artificial neural network , 1999 .
[26] Ping-Feng Pai,et al. Recurrent Support Vector Machines in Reliability Prediction , 2005, ICNC.
[27] Bernhard Schölkopf,et al. A tutorial on support vector regression , 2004, Stat. Comput..
[28] J. Spall. Implementation of the simultaneous perturbation algorithm for stochastic optimization , 1998 .
[29] Oscar Castillo,et al. Optimization of ensemble neural networks with type-2 fuzzy response integration for predicting the Mackey-Glass time series , 2013, 2013 World Congress on Nature and Biologically Inspired Computing.
[30] Simon Haykin,et al. Support vector machines for dynamic reconstruction of a chaotic system , 1999 .
[31] Sheng-wei Fei,et al. Fault diagnosis of power transformer based on support vector machine with genetic algorithm , 2009, Expert Syst. Appl..
[32] P. Siarry,et al. Gradient descent method for optimizing various fuzzy rule bases , 1993, [Proceedings 1993] Second IEEE International Conference on Fuzzy Systems.
[33] Elsayed A. Elsayed,et al. Overview of Reliability Testing , 2012, IEEE Transactions on Reliability.
[34] Erdem Acar. Reliability prediction through guided tail modeling using support vector machines , 2013 .
[35] James T. Kwok. Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression , 2001, ICANN.
[36] Oscar Castillo,et al. Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic , 2013, Expert Syst. Appl..
[37] Darrell Whitley,et al. A genetic algorithm tutorial , 1994, Statistics and Computing.
[38] Ping-Feng Pai,et al. Software reliability forecasting by support vector machines with simulated annealing algorithms , 2006, J. Syst. Softw..
[39] Greg Welch,et al. An Introduction to Kalman Filter , 1995, SIGGRAPH 2001.
[40] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[41] Junyan Yang,et al. Application Research of Support Vector Machines in Condition Trend Prediction of Mechanical Equipment , 2005, ISNN.
[42] Christopher J. C. Burges,et al. A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.