Adaptive fuzzy wavelet neural network filter for hand tremor canceling in microsurgery

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 new kind of nonlinear adaptive filter, the fuzzy wavelet neural network filter (FWNNF), is proposed to alleviate this problem. The proposed FWNNF is inherently a feed forward multilayered connectionist network which can learn by itself according to numerical training data or expert knowledge represented by fuzzy if-then rules. Each rule includes a wavelet function in the consequent part of the rule. In the parameter learning phase, gradient descent method is used to minimize the output error. With the FWNNF, we can model and predict the hand tremor more effectively and improve the precision and reliability in the master-slave robotic system for microsurgery. Extensive simulation results are given and performance comparison with traditional methods is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.

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