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Robust g-filter using support vector method

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URI: https://hdl.handle.net/10016/7264
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2004.07.003
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robust_camps-valls_N_2004_ps.pdf (182.05 KB)
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2004-12
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Elsevier
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This Letter presents a new approach to time series modelling using the support vector machines (SVM). Although the g filter can provide stability in several time series models, the SVM is proposed here to provide robustness in the estimation of the g filter coefficients. Examples in chaotic time series prediction and channel equalization show the advantages of the joint SVM g filter.
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Suuport vector machines, Gamma filter, Iterated prediction, Channel equalization
Bibliographic citation
Neurocomputing, vol. 62, Dec. 2004, pp. 493-499
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