步行器精密测力系统中的人工神经网络标定新技术  

Novel Calibration Technique for Precision Walker Dynamometer System Based on Artificial Neural Network

作　　者：明东[1,2] 张希[1] 戴岳刚[1] 周仲兴[1] 万柏坤[1] 胡勇[2] 王威杰[3] 

机构地区：[1]天津大学精密仪器与光电子工程学院,天津300072 [2]香港大学矫形及创伤外科系,香港999077 [3]邓迪大学运动分析研究所

出　　处：《纳米技术与精密工程》2009年第3期245-248,共4页Nanotechnology and Precision Engineering

基　　金：国家高技术研究发展计划(863)项目(2007AA04Z236);国家自然科学基金资助项目(60501005);天津市科技支撑计划重点项目(07ZCKFSF0130008ZCGHHZ00300)

摘　　要：设计了一种可用于步行器精密测力系统标定的3层反向传播人工神经网络模型,以测力系统12导联应变片电桥输出电压作为网络输入向量,6个负载分量力作为网络输出向量,并通过目标误差下的绝对误差和对比确定出模型隐含层的最优神经元数.有关误差校验结果表明,使用该神经网络技术标定后的系统最大单向力精度误差为7.78%,最大交叉干扰为7.49%,与传统的线性标定方法相比,能够有效提高步行器受载力的测量精度并大大降低干扰误差,未来有望为步行器助行康复训练效果的准确监控和评估提供帮助.A back-propagation artificial neural network model with three layers was developed for the calibration of precision walker dynamometer system. This model adopted the output voltages from 12-channel strain gauge bridges in the dynamometer system as the network input vector and the six load components as the network output vector. The neuron number of the single hidden layer in this model was optimized by comparing the absolute error summations under the target error. Relevant error check results showed that, after the calibration using this neural network, the maximal system precision error with single-direction force was 7.78% and the maximal crosstalk was 7.49% . In comparison with traditional linear calibration method, the proposed technique can effectively increase the measurement precision of walker loads and greatly decrease the erosstalk error, which might be helpful for accurately monitoring and evaluating the rehabilitation training effect of walker-assisted walking in the future.

关 键 词：步行器 标定 人工神经网络 

分 类 号：TH823[机械工程—仪器科学与技术]