Paper Title:
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.

  Info
Periodical
Edited by
Honghua Tan
Pages
2603-2607
DOI
10.4028/www.scientific.net/AMM.29-32.2603
Citation
C. Y. Xiao, P. Y. Guo, Z. P. Feng, Y. S. Deng, "Research on Forecasting Method Based on Genetic Algorithms and Support Vector Machines ", Applied Mechanics and Materials, Vols. 29-32, pp. 2603-2607, 2010
Online since
August 2010
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