基于SAW微力传感器的GRNN拟合研究  

Study of GRNN curve fitting based on SAW micro force sensor

作　　者：季雪咪 李媛媛[1] 李济同 JI Xuemi;LI Yuanyuan;LI Jitong(College of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)

机构地区：[1]上海工程技术大学电子电气工程学院

出　　处：《传感器与微系统》2019年第12期14-17,共4页Transducer and Microsystem Technologies

基　　金：国家自然科学基金资助项目(61803254)

摘　　要：针对传统方法拟合声表面波(SAW)微力传感器输入-输出曲线的算法为最小二乘法,但无法进行全局搜索,易获得局部最优解的不足,基于SAW微力传感器实测数据,采用广义回归神经网络(GRNN)进行曲线拟合。选取以铌酸锂为压电基底的SAW微力传感器,对其施加微压并通过网络分析仪测量输出频率数据,依据GRNN和最小二乘法基本原理采用MATLAB R2016b分别对频率-压力数据进行拟合并对比。仿真结果表明:与最小二乘法相比,GRNN误差明显减小,约一个数量级,能有效提高拟合精度。The least square method is often used to fit the input-output curve of the surface acoustic wave(SAW)micro-force sensor,however,the global search cannot be performed,and the local optimal solution is easy to obtain. Aiming at the shortcomings of traditional methods,based on the measured data of SAW micro force sensors,the generalized regression neural networks(GRNN) are used to fit the curve. The SAW micro force sensor with lithium niobate as the piezoelectric substrate is selected,and the micro force is applied to it. The output frequency data is measured by the network analyzer. The frequency-pressure data is fitted by MATLAB R2016 b according to the basic principle of GRNN and least squares method. The simulation results show that compared with the least squares method,the error of GRNN is significantly reduced,about an order of magnitude,which can effectively improve the fitting precision.

关 键 词：广义回归神经网络 最小二乘法 声表面波微力传感器 曲线拟合 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]