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Vol. 150, No. 3, March 2013, pp. 18-26

 

Bullet

 

The Study of Decoupling Methods for a Novel Tactile Sensor Based on BP Neural Network

 

1, 2, 4 Feilu Wang, 1, 2 Xuekun Zhuang, 2 Xin Sun, 2 Quangjun Song, 2 Hongqing Pan, 3 Yong Yu, 1, 2 * Feng Shuang

1 Department of Automation, University of Science and Technology of China, Hefei, 230027, China

2 Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China

3 Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065, Japan

4 School of Electronic and Information Engineering, Anhui University of Architecture, Hefei, 230601, China

* E-mail: shuangfeng@ustc.edu.cn

 

Received: 5 November 2013   /Accepted: 19 March 2013   /Published: 29 March 2013

Digital Sensors and Sensor Sysstems

 

Abstract: This paper proposes a decoupling method for a novel tactile sensor based on improved Back Propagation Neural Network (BPNN). In the numerical experiments, the number of hidden layer nodes of the BPNN is optimized and k-fold-cross-validation (k-CV) method is also applied to construct the dataset. Furthermore, information of the tactile sensor array at different scales is used to construct the BPNN, which enhances the performance greatly. Numerical simulations show that the BPNN with strong nonlinear approximation ability plays an important role in decoupling mapping relationship between resistance and deformation of the tactile sensor, which significantly increases the decoupling accuracy and satisfies the real-time requirements of the multi-dimensional tactile sensor array

 

Keywords: Tactile sensor, BP neural network, Decoupling method, k-CV

 

 

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