摘要
针对三轴加速度计存在的测量误差,建立了隐式非线性误差模型,并提出一种自主反向调优的极限学习机(RT-ELM)对误差模型进行训练。实验结果表明:三轴补偿后误差基本控制在±0. 07 m/s^2范围内,均方根误差小于0. 004 m/s^2,误差比补偿前减小超过100倍,补偿精度是固定型极限学习机ELM的7倍左右。任意选取训练集和测试集补偿效果基本一致,证明超限学习算法具有很好的泛化能力和鲁棒性,而且几千个样本点的训练时间仅0. 06 s左右,其速度是传统反向传播(BP)神经网络的上千倍,适用于对实时性要求较高的误差补偿和控制系统等领域。
An implicit nonlinear error model is established for the measurement errors of three-axis accelerometers,and an auto reverse tuning extreme learning machine(RT-ELM)is proposed to train the error model.The experimental results show that the error after compensation is basically controlled within±0.07 m/s^2,the root-mean-square error is less than 0.004 m/s^2,and the error is reduced by more than one hundred times before the compensation,which is higher than the fixed ELM about 7 times.Compensation effect are basically the same,selected training set and test set randomly,it proves that the extreme learning algorithm has good generalization ability and robustness.The training time for thousands of sample points is only about 0.06 s,and its speed is thousands of times higher than that of traditional back propagation(BP)neural networks.It is suitable for fields such as error compensation and control systems that require high real-time performance.
作者
刘艳霞
方建军
石岗
LIU Yanxia;FANG Jianjun;SHI Gang(College of Urban Rall Transit and Logistics,Beijing Union University,Beijing100101,China;College of Information and Control Engineering,China University of Petroleum,Qingdao266580,China)
出处
《传感器与微系统》
CSCD
2019年第7期138-141,共4页
Transducer and Microsystem Technologies
基金
国家自然科学基金资助项目(61602041)
北京联合大学人才强校优选计划资助项目(BPHR2017CZ07)
北京联合大学新起点后期资助项目(Hzk10201601)
关键词
极限学习机
神经网络
非线性误差模型
加速度计
extreme learning machine(ELM)
neural network
nonlinear error model
accelerometer
