In this paper, we demonstrate the use of support vector regression (SVR) techniques for black-box system identification. There methods derive from statistical learning theory, and are of great theoretical and practical interest. We briefly describe the theory underpinning SVR, and compare support vector methods with other approaches using radial basis networks. Finally, we apply SVR to modeling the behaviour of a hydralic robot arm, and show that SVR improves on previously published results.

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In: 11th IEEE Workshop on Statistical Signal Processing

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