LS-SVM Based Self-Learning Tremor Controller for Microsurgery

Zhi Liu; Qi Hang Wu; Yun Zhang

doi:10.4028/www.scientific.net/AMR.255-260.1999

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 255-260LS-SVM Based Self-Learning Tremor Controller for...

LS-SVM Based Self-Learning Tremor Controller for Microsurgery

Abstract:

One of the main problems for effective control of a minimally invasive surgery (MIS) is the imprecision that caused by hand tremor. In this paper, a novel adaptive filter, the least squares support vector machines adaptive filter (LS-SVMAF), is proposed to overcome this problem. Compared with traditional methods like multi-layer perceptron (MLP), LS-SVM shows a superior performance of nonlinear modeling with small scale of data set or high dimensional input space. Simulation results demonstrate the effectiveness of the proposed filter and its superior performance over its competing rivals.

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Periodical:

Advanced Materials Research (Volumes 255-260)

Pages:

1999-2003

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.255-260.1999

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Online since:

May 2011

Authors:

Zhi Liu, Qi Hang Wu, Yun Zhang

Keywords:

Hand Tremor, Least Squares Support Vector Machines (Ls-SVM), Minimally Invasive Surgery (MIS), Multi-Layer Perceptron (MLP)

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References

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