Determining Parameters of Support Vector Machines by Genetic AlgorithmsApplications to Reliability Prediction

Due to the lack of a structure way in determining the free parameters of support vector machines (SVMs), this study uses genetic algorithms (GAs) to select parameters of SVMs. In addition, the developed SVMG (support vector machine with genetic algorithms) model is applied to reliability prediction. Two numerical examples in the literature are employed to illustrate the performances of various prediction models. Empirical results reveal that the proposed model provides more accurate prediction results than the other forecasting models. Therefore, the presented SVMG model offers a promising alternative in reliability prediction.

[1]  John E. Angus,et al.  Optimal mutation probability for genetic algorithms , 1995 .

[2]  Ping-Feng Pai,et al.  A hybrid ARIMA and support vector machines model in stock price forecasting , 2005 .

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

[4]  Mohamed Mohandes,et al.  Support vector machines for wind speed prediction , 2004 .

[5]  Wenjian Wang,et al.  Air pollutant parameter forecasting using support vector machines , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[6]  Quan J. Wang,et al.  Using genetic algorithms to optimise model parameters , 1997 .

[7]  R. Sitte.  Comparison of software-reliability-growth predictions: neural networks vs parametric-recalibration , 1999 .

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

[9]  J. T. Duane.  Learning Curve Approach to Reliability Monitoring , 1964, IEEE Transactions on Aerospace.

[10]  F. Tay,et al.  Application of support vector machines in financial time series forecasting , 2001 .

[11]  Wei-Chiang Hong.  Support Vector Machines with Genetic Algorithms in Forecasting Electricity Load , 2006 .

[12]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.

[13]  Francis Eng Hock Tay,et al.  Modified support vector machines in financial time series forecasting , 2002, Neurocomputing.

[14]  Theodore B. Trafalis,et al.  Support vector machine for regression and applications to financial forecasting , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[15]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[16]  Vojislav Kecman,et al.  Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

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

[19]  Thong Ngee Goh,et al.  A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction , 2002 .

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

[21]  Min Xie,et al.  The use of ARIMA models for reliability forecasting and analysis , 1998 .

[22]  Alan F. Murray,et al.  International Joint Conference on Neural Networks , 1993 .