Dynamic modeling of wrist force sensors using least squares support vector machines

Xiaodong Wang; Weifeng Liang; Jinshan Wang

doi:10.1117/12.717831

28 October 2006 Dynamic modeling of wrist force sensors using least squares support vector machines

Xiaodong Wang, Weifeng Liang, Jinshan Wang

Author Affiliations +

Proceedings Volume 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation; 63581A (2006) https://doi.org/10.1117/12.717831
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China

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.

Citation Download Citation

Xiaodong Wang, Weifeng Liang, and Jinshan Wang "Dynamic modeling of wrist force sensors using least squares support vector machines", Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 63581A (28 October 2006); https://doi.org/10.1117/12.717831

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