Research on Forecasting Method Based on Genetic Algorithms and Support Vector Machines

Abstract:

State forecast of machine using support vector machines has good generalization ability in situation of rare samples. Appropriate parameter selection is very crucial to the learning results and generalization ability of support vector machines. In addition, embedding dimension influences the phase space reconstitution of nonlinear systems, as well as the precision of machine state forecasting. In this paper, an approach to optimize the parameters of SVM and the embedding dimension based on genetic algorithms was proposed. The proposed model is applied to the tendency forecasting of the vibration of shovel electric drive system. The results show that it can avoid blindness of manually selection of parameters and meanwhile improves the prediction performance greatly.

• Periodical: Applied Mechanics and Materials (Volumes 29-32)
• Pages: 2603-2607
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.29-32.2603
• Online since: August 2010
• Authors: Cheng Yong Xiao, Peng Yan Guo, Zhi Peng Feng, Yong Sheng Deng
• Keywords: Genetic Algorithm (GA), Least Squares Support Vector Machines (Ls-SVM), Machine State Prediction, Time-Series

References

[1] L.Z. Li, Z.S. Zhang and Z.J. He: Journal of Xi'an Jiaotong University，Vol. 38 (2004), p.230(In Chinese).

[2] H.J. Wang and X.L. Xu: Journal of Lanzhou University of Technology, Vol. 31 (2005), p.36. (In Chinese).

[3] Y. Liu, L.S. Kang and S.P. Chen.: �on-numerical Parallel algorithm-Genetic Algorithms. (science press, Chinese 1997).

[4] J.Y. Zhu: Acta Aeronauticaet Astronautica Sinica, Vol. 25 (2004), p.565(In Chinese).

[5] Y.X. Yang，D. Liu. Power: System Technology, Vol. 29 (2005), p.60(In Chinese).

[6] H. Yan, X. G, Zhang and Y.D. Li: Journal of Qinghua University, Vol. 41 (2001), p.77(In Chinese).

[7] Goldberg D. Genetic algorithms in search, optimization and machine learning . Addison Wesley, Reading, MA, (1989).