Smoothness priors support vector method for robust system identification

Author:

Tötterman S.,Toivonen H.T.

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Control and Optimization,Computer Science Applications,Human-Computer Interaction,Control and Systems Engineering

Reference24 articles.

1. Vapnik, V.: ‘The nature of statistical learning theory’, (Springer-Verlag, New York 1995)

2. Support Vector Method for RobustARMA System Identification

3. Mattera, D., and Haykin, S.: ‘Support vector machines for dynamic reconstruction of a chaotic system’, Schölkopf, B., Advances in Kernel methods – support vector learning, (MIT Press, Cambridge, MA 1999), p. 211–241

4. Müller, K.-R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., and Vapnik, V.: ‘Predicting time series with support vector machines’, Schölkopf, B., , et al. (Eds.) Advances in Kernel methods – support vector learning, (MIT Press, Cambridge, MA 1999), p. 243–254

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Least-squares Support Vector Regression for Identification of Periodic Systems;IFAC Proceedings Volumes;2010

2. Adaptive control and signal processing literature survey (no. 13);International Journal of Adaptive Control and Signal Processing;2009-08

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