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. 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.
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Acknowledgments
The authors would like to thank the editor and reviewers for their very insightful and constructive comments. This work was supported by the National Natural Science Foundation of China under Project U0735003 and 60974047, the Natural Science Foundation of Guangdong Province under Project 8351009001000002 and 9151009001000011, Science and Technology Plan Projects of Guangdong Province under Project 2009B010900051, FOK Ying Tung Education Foundation of China under Project 121061, and High-Level Professionals Project of Guangdong Province.
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Liu, Z., Wu, Q., Zhang, Y. et al. Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery. Int. J. Mach. Learn. & Cyber. 2, 37–47 (2011). https://doi.org/10.1007/s13042-011-0012-5
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DOI: https://doi.org/10.1007/s13042-011-0012-5