A three-domain fuzzy wavelet network filter using fuzzy PSO for robotic assisted minimally invasive surgery

Abstract A three-domain fuzzy wavelet network filter (3DFWNF) is proposed to filter the physiological tremor in robotic assisted microsurgical procedures, which bases on the three-domain fuzzy wavelet neural network (3DFWN) for estimating the modulated signals with multiple frequency components. The fuzzy domain is added in the 3DFWN to handle the fuzzy uncertainties of the tremor signals. The adaptive parameters of the network are adjusted by using a novel particle swarm optimization (PSO) algorithm in the training process, namely fuzzy PSO (FPSO). FPSO adopts fuzzy sets described by Gaussian membership function to define the position and velocity of particles, thus all arithmetic operators in the position and velocity updating rules used in the original PSO are replaced by the operators and procedures defined on fuzzy sets. Without the necessity for gradients, the FPSO coordinates the exploration and exploitation capabilities of particles, ensures quick convergence and a preferable global search. The proposed filter is compared with the existing RBF neural network and fuzzy wavelet neural networks. Experiments are carried in different situations, experimental results show superiority on tremor suppression of the newly filter. The effectiveness and accuracy of the FPSO algorithm are also verified.