基于神经网络的光纤布拉格光栅触觉信号解耦研究  

Research on decoupling of fiber Bragg grating tactile signal based on neural network

作　　者：钱牧云 齐瑜 魏新园 Qian Muyun;Qi Yu;Wei Xinyuan(College of Electrical and Information Engineering,Anhui University of Technology,Ma'anshan 243032,China)

机构地区：[1]安徽工业大学电气与信息工程学院,马鞍山243032

出　　处：《仪器仪表学报》2021年第8期44-51,共8页Chinese Journal of Scientific Instrument

基　　金：安徽省自然科学基金(1908085QF294)项目资助。

摘　　要：针对应用于电子皮肤的柔性光纤布拉格光栅触觉传感器的输出信号是施加载荷位置、大小等信息的非线性、多维耦合问题,在对光纤布拉格光栅触觉传感阵列进行力学有限元仿真的基础上,提出了将误差逆传播神经网络和径向基函数神经网络应用于仿真和实验的触觉信号解耦方法。对实验数据的神经网络解耦结果表明,相对于误差逆传播神经网络,径向基函数神经网络具有更强的抗噪声能力,能够更好地逼近含有噪声的触觉多维非线性实验数据之间的映射关系。经径向基函数神经网络解耦后,传感器阵列的空间分辨率为5 mm,对压力位置和大小感知的最小相对误差为3.00%和4.82%。本文的研究成果对开展电子皮肤柔性触觉传感器的研究和推广具有一定的实用价值。Aiming at the problem that the output signal of a flexible fiber Bragg grating tactile sensor applied to the electronic skin is the nonlinear and multi-dimensional coupling of the position and size information of the applied load,based on the mechanical finite element simulation of the fiber Bragg grating tactile sensing array,the decoupling methods are proposed,in which error back propagation(BP)neural network and radial basis function(RBF)neural network are applied to the haptic signals in simulation and experiment.The neural network decoupling results of experiment data show that compared with the error BP neural network,the RBF neural network has stronger anti-noise ability and can better approximate the mapping relationship between noisy tactile multi-dimensional nonlinear experiment data.After decoupling with the radial basis function neural network,the spatial resolution of the sensor array is 5 mm,and the minimum relative errors of the pressure position and size perception are 3.00%and 4.82%.The research results in this paper have certain practical value for the research and promotion of electronic skin flexible tactile sensors.

关 键 词：神经网络 光纤布拉格光栅 柔性 触觉 解耦 

分 类 号：TN247[电子电信—物理电子学] TH741[机械工程—光学工程]