Abstract
In order to resolve the noise reduction in chaotic time series, a novel method based on Multi-dimensional version of Recurrent Least Square Support Vector Machine(MDRLS-SVM) is proposed in this paper. By analyzing the relationship between the function approximation and the noise reduction, we realized that the noise reduction can be implemented by the function approximation techniques. On the basis of the MDRLS-SVM and the reconstructed embedding phase theory, the function approximation in the high dimensional embedding phase space is carried out and the noise reduction achieved simultaneously.
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Sun, J., Zhou, Y., Bai, Y., Luo, J. (2006). Nonlinear Noise Reduction of Chaotic Time Series Based on Multi-dimensional Recurrent Least Squares Support Vector Machines. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_100
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DOI: https://doi.org/10.1007/11893028_100
Publisher Name: Springer, Berlin, Heidelberg
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