DocumentCode :
2707841
Title :
Radical basis function neural network for a novel tactile sensor design
Author :
Zhuang, Xuekun ; Sun, Xin ; Pan, Hongqing ; Wang, Yubing ; Ge, Yunjian ; Shuang, Feng
Author_Institution :
Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China
fYear :
2012
fDate :
6-8 June 2012
Firstpage :
926
Lastpage :
931
Abstract :
In this paper, a decoupling algorithm based on the RBF (radical basis function) network is proposed. Compared to the Levenberg-Marquardt (L-M) algorithm and CMA-ES algorithm, numerical experiments show that the decoupling algorithm based on the radial basis function network is of higher performance on both accuracy and time complexity, which makes the real-time property of the tactile sensor possible. In the further research, the mapping between the force and the deformation of the tactile sensor surface can be revealed by the ANSYS simulation, and the RBF network plays a key role in the digital model of the tactile sensor.
Keywords :
computational complexity; covariance matrices; evolutionary computation; radial basis function networks; tactile sensors; ANSYS simulation; CMA-ES algorithm; L-M algorithm; Levenberg-Marquardt algorithm; RBF network; accuracy; decoupling algorithm; digital model; numerical experiments; radial basis function neural network; real-time property; tactile sensor design; tactile sensor surface; time complexity; Accuracy; Algorithm design and analysis; Numerical models; Rubber; Surface treatment; Tactile sensors; Wires; Flexible tactile sensor; Pressure-conductive rubber; RBF;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Automation (ICIA), 2012 International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4673-2238-6
Electronic_ISBN :
978-1-4673-2236-2
Type :
conf
DOI :
10.1109/ICInfA.2012.6246948
Filename :
6246948
Link To Document :
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