Failure and reliability prediction by support vector machines regression of time series data

[1]  Enrico Zio,et al.  Failure and Reliability Predictions by Infinite Impulse Response Locally Recurrent Neural Networks , 2012 .

[2]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[3]  Enrique López Droguett,et al.  Mathematical formulation and numerical treatment based on transition frequency densities and quadrature methods for non-homogeneous semi-Markov processes , 2009, Reliab. Eng. Syst. Saf..

[4]  Enrico Zio,et al.  Quantifying uncertainties in the estimation of safety parameters by using bootstrapped artificial neural networks , 2008 .

[5]  Vasiliy V. Krivtsov,et al.  Recent advances in theory and applications of stochastic point process models in reliability engineering , 2007, Reliab. Eng. Syst. Saf..

[6]  Kuan-Yu Chen,et al.  Forecasting systems reliability based on support vector regression with genetic algorithms , 2007, Reliab. Eng. Syst. Saf..

[7]  James Kennedy,et al.  Defining a Standard for Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[8]  Gregory Levitin,et al.  Robust recurrent neural network modeling for software fault detection and correction prediction , 2007, Reliab. Eng. Syst. Saf..

[9]  Cheng-Hua Wang,et al.  Support vector regression with genetic algorithms in forecasting tourism demand , 2007 .

[10]  E. Zio,et al.  A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes , 2006, IEEE Transactions on Nuclear Science.

[11]  Ping-Feng Pai,et al.  Software reliability forecasting by support vector machines with simulated annealing algorithms , 2006, J. Syst. Softw..

[12]  Ping-Feng Pai,et al.  System reliability forecasting by support vector machines with genetic algorithms , 2006, Math. Comput. Model..

[13]  Ping-Feng Pai,et al.  Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting , 2005 .

[14]  Liang Tian,et al.  On-line prediction of software reliability using an evolutionary connectionist model , 2005, J. Syst. Softw..

[15]  Lipo Wang,et al.  Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing) , 2005 .

[16]  Vojislav Kecman,et al.  Support Vector Machines – An Introduction , 2005 .

[17]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[18]  Liang Tian,et al.  Evolutionary neural network modeling for software cumulative failure time prediction , 2005, Reliab. Eng. Syst. Saf..

[19]  Anthony Widjaja,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.

[20]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[21]  Laurent Doyen,et al.  Classes of imperfect repair models based on reduction of failure intensity or virtual age , 2004, Reliab. Eng. Syst. Saf..

[22]  Lutgarde M. C. Buydens,et al.  Using support vector machines for time series prediction , 2003 .

[23]  Thong Ngee Goh,et al.  A study of the connectionist models for software reliability prediction , 2003 .

[24]  Waltraud Kahle,et al.  A general repair, proportional-hazards, framework to model complex repairable systems , 2003, IEEE Trans. Reliab..

[25]  Loon Ching Tang,et al.  Application of neural networks in forecasting engine systems reliability , 2003, Appl. Soft Comput..

[26]  Mohammad Modarres,et al.  Generalized renewal process for analysis of repairable systems with limited failure experience , 2002, Reliab. Eng. Syst. Saf..

[27]  David Zhang,et al.  On the neural network approach in software reliability modeling , 2001, J. Syst. Softw..

[28]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[29]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[30]  Tadashi Dohi,et al.  Optimal software release scheduling based on artificial neural networks , 1999, Ann. Softw. Eng..

[31]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[32]  Gunnar Rätsch,et al.  Predicting Time Series with Support Vector Machines , 1997, ICANN.

[33]  F. Girosi,et al.  Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[34]  W. Fuller,et al.  Introduction to Statistical Time Series (2nd ed.) , 1997 .

[35]  Chao Ton Su,et al.  Combining time series and neural network approaches for modeling reliability growth , 1997 .

[36]  L. Darrell Whitley,et al.  Prediction of Software Reliability Using Connectionist Models , 1992, IEEE Trans. Software Eng..

[37]  Ah Chung Tsoi,et al.  FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling , 1991, Neural Computation.

[38]  Sheldon M. Ross,et al.  Introduction to Probability Models (4th ed.). , 1990 .

[39]  Hassan Zahedi,et al.  Repairable Systems Reliability: Modeling, Inference, Misconceptions and Their Causes , 1989 .

[40]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[41]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .