Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery

被引：40

作者：

Liu, Zhi ^{[1
]}

Wu, Qihang ^{[1
]}

Zhang, Yun ^{[1
]}

Chen, C. L. Philip ^{[2
,3
]}

机构：

[1] Guangdong Univ Technol, Dept Automat, Guangzhou 510006, Guangdong, Peoples R China

[2] Univ Macau, Fac Sci & Technol, Taipa, Macau, Peoples R China

[3] Univ Texas San Antonio, Dept Elect & Comp Engn, San Antonio, TX 78249 USA

来源：

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS | 2011年 / 2卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Minimally invasive surgery (MIS); Least squares support vector machines (LS-SVM); Multi layer perceptron (MLP); Hand tremor;

D O I：

10.1007/s13042-011-0012-5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. With the LS-SVMAF, we can model and predict the hand tremor more effectively and improve the precision and reliability in the master-slave robotic system for microsurgery. Simulation results demonstrate the effectiveness of the proposed filter and its superior performance over its competing rivals.

引用

页码：37 / 47

页数：11

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