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Proceedings Paper

Dynamic modeling of wrist force sensors using least squares support vector machines
Author(s): Xiaodong Wang; Weifeng Liang; Jinshan Wang
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Paper Abstract

The least squares support vector machines (LS-SVMs) are proposed for nonlinear dynamic modeling of wrist force sensors. The LS-SVMs are established based on the structural risk minimization principle rather than minimize the empirical error commonly implemented in the neural networks, the LS-SVMs can achieve higher generalization performance. Also, local minima and over fitting are unlikely to occur. Therefore, the LS-SVMs can overcome the shortcoming of neural networks in dynamic modeling of wrist force sensors. The effectiveness and reliability of the method are demonstrated by applying it to the examples. The experimental results show that the method is still effective even if the sensor dynamic model is nonlinear.

Paper Details

Date Published: 28 October 2006
PDF: 6 pages
Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63581A (28 October 2006); doi: 10.1117/12.717831
Show Author Affiliations
Xiaodong Wang, Zhejiang Normal Univ. (China)
Weifeng Liang, Zhejiang Normal Univ. (China)
Jinshan Wang, Zhejiang Normal Univ. (China)


Published in SPIE Proceedings Vol. 6358:
Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation

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