硅微陀螺漂移混沌特性分析及预测  

Chaotic Analysis and Prediction for Random Drift of Silicon Micro-Machined Gyroscope

作　　者：吉训生[1,2] 王寿荣[2] 

机构地区：[1]东南大学仪器科学与工程学院,江苏南京210096 [2]江南大学物联网工程学院,江苏无锡214122

出　　处：《中北大学学报（自然科学版）》2011年第5期567-573,共7页Journal of North University of China(Natural Science Edition)

基　　金：国家自然科学基金资助项目(60674092)

摘　　要：硅微陀螺漂移具有混沌特性,可以通过相空间重构对漂移进行预测.计算出硅微陀螺漂移序列的Lyapunov指数为0.000 529,估计出随机漂移可预测的时间尺度为1 890,求出相空间重构所需的延迟时间、关联维数和嵌入维数分别为57,7.042和15.以相空间重构后的漂移序列为输入变量,提出利用RBF神经网络和陀螺阵列技术,对陀螺静态测试和动态测试时的随机漂移序列进行预测.预测结果表明:基于相空间重构的陀螺静态和动态测试情况下的预测精度可分别提高5.39和2.65倍,优于常用的时序法和未经相空间重构的神经网络法.The drift of the silicon micro-machined gyroscope features chaotic property,which can be predicted by phase space reconstruction.The drift of a certain gyroscope was calculated.Its Lyapunov exponent was 0.000 529 and the predictable step number of the drift was 1 890.The delaying time,embedding dimension and correlation dimension were also calculated by 57,7.042 and 15 respectively.Based on the phase space reconstruction,RBF neural network and the gyroscope array technology were applied to predict the static and dynamic drifts of the gyroscope.The accuracy of the static drift prediction and the dynamic drift prediction were improved by 5.39 and 2.65 times.The prediction accuracy was superior to the time series method and traditional RBF neural network.

关 键 词：混沌 硅微陀螺 RBF神经网络 LYAPUNOV指数 漂移 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置]