传感技术学报
傳感技術學報
전감기술학보
Chinese Journal of Sensors and Actuators
2015年
10期
1520-1524
,共5页
田颖%汪立新%李灿%陈伟
田穎%汪立新%李燦%陳偉
전영%왕립신%리찬%진위
陀螺漂移%建模预测%集合经验模态分解%相关向量机
陀螺漂移%建模預測%集閤經驗模態分解%相關嚮量機
타라표이%건모예측%집합경험모태분해%상관향량궤
gyro drift%modelingprediction%ensemble empirical mode decomposition%relevance vector machine
陀螺漂移序列具有非平稳和非线性的特点,针对单一模型难以对其实现精确预测的问题,提出一种基于集合经验模态分解(EEMD)和相关向量机(RVM)的混合建模方法,实现对陀螺漂移序列的区间预测.首先,利用集合经验模态分解将漂移序列分解为多个模态和一个余量;将模态区分为噪声和趋势两个分量,对噪声分量建立分布模型,对趋势分量建立RVM模型,两者等权相加还原得混合模型;最后,给定置信度,得到置信区间预测结果.将该方法用于某振动陀螺漂移序列预测实例,结果表明:该混合预测模型能准确预测陀螺漂移,其中RVM的预测精度达到99.86%,且验证集以给定的置信度落在预测区间内,可为陀螺的寿命预测和性能分析提供依据.
陀螺漂移序列具有非平穩和非線性的特點,針對單一模型難以對其實現精確預測的問題,提齣一種基于集閤經驗模態分解(EEMD)和相關嚮量機(RVM)的混閤建模方法,實現對陀螺漂移序列的區間預測.首先,利用集閤經驗模態分解將漂移序列分解為多箇模態和一箇餘量;將模態區分為譟聲和趨勢兩箇分量,對譟聲分量建立分佈模型,對趨勢分量建立RVM模型,兩者等權相加還原得混閤模型;最後,給定置信度,得到置信區間預測結果.將該方法用于某振動陀螺漂移序列預測實例,結果錶明:該混閤預測模型能準確預測陀螺漂移,其中RVM的預測精度達到99.86%,且驗證集以給定的置信度落在預測區間內,可為陀螺的壽命預測和性能分析提供依據.
타라표이서렬구유비평은화비선성적특점,침대단일모형난이대기실현정학예측적문제,제출일충기우집합경험모태분해(EEMD)화상관향량궤(RVM)적혼합건모방법,실현대타라표이서렬적구간예측.수선,이용집합경험모태분해장표이서렬분해위다개모태화일개여량;장모태구분위조성화추세량개분량,대조성분량건립분포모형,대추세분량건립RVM모형,량자등권상가환원득혼합모형;최후,급정치신도,득도치신구간예측결과.장해방법용우모진동타라표이서렬예측실례,결과표명:해혼합예측모형능준학예측타라표이,기중RVM적예측정도체도99.86%,차험증집이급정적치신도락재예측구간내,가위타라적수명예측화성능분석제공의거.
In view that the timeseries of gyro drift cannot be preciselypredicted by single forecasting model due to its non-linear and non-stationary characteristics,interval forecasting for gyro drift series can be obtainedwith hybrid modeling method based on ensemble empirical mode decomposition(EEMD)and relevance vector machine(RVM)which is proposed. Firstly,the drift data is decomposed into a series of intrinsic mode function and one residue via EEMD. Secondly,modes areclassified into two categories:noise component and trend component,the distributionmodel of noise component and the RVM model of trend componentis established,two models are added with equal weight to establish the hybrid model. In the end,we set the confidence coefficient to obtain interval forecasting. By using the proposed method for a vibratory gyro drift prediction,the experiment result shows:in hybrid model,RVM prediction accuracy is 99.86%,validation set is contained by prediction interval with designated confidence coefficient. The hybrid model could provide reliable evidence for life prediction and performanceanalysis of gyro.