南京航空航天大学学报（英文版）
南京航空航天大學學報（英文版）
남경항공항천대학학보（영문판）
TRANSACTIONS OF NANJING UNIVERSITY OF AERONATICS & ASTRONAUTICS
2010年
3期
226-231
,共6页
模型辨识%分布式Kalman滤波%后向传播神经网络%静电陀螺
模型辨識%分佈式Kalman濾波%後嚮傳播神經網絡%靜電陀螺
모형변식%분포식Kalman려파%후향전파신경망락%정전타라
model identification%distributed Kalman filter(DKF)%back propagation neural network(BPNN)%electrostatic suspended gyroscope(ESG)
提出一种新的方法,把分布式Kalman滤波(DKF)方法与后向传播神经网络(BPNN)技术相结合,用于静电陀螺漂移的模型辨识.首先,为了消除测量噪声影响,将同一个静电陀螺带有噪声的多次测量数据集映射到一个虚拟的传感器网络中,然后采用具有嵌入式紧致滤波功能的DKF对映射数据进行滤波预处理.在此基础上,将预处理结果转换为用于训练神经网络的输入数据和输出数据,然后采用BPNN辨识静电陀螺漂移.实验表明,该方法可有效用于陀螺漂移的模型辨识.
提齣一種新的方法,把分佈式Kalman濾波(DKF)方法與後嚮傳播神經網絡(BPNN)技術相結閤,用于靜電陀螺漂移的模型辨識.首先,為瞭消除測量譟聲影響,將同一箇靜電陀螺帶有譟聲的多次測量數據集映射到一箇虛擬的傳感器網絡中,然後採用具有嵌入式緊緻濾波功能的DKF對映射數據進行濾波預處理.在此基礎上,將預處理結果轉換為用于訓練神經網絡的輸入數據和輸齣數據,然後採用BPNN辨識靜電陀螺漂移.實驗錶明,該方法可有效用于陀螺漂移的模型辨識.
제출일충신적방법,파분포식Kalman려파(DKF)방법여후향전파신경망락(BPNN)기술상결합,용우정전타라표이적모형변식.수선,위료소제측량조성영향,장동일개정전타라대유조성적다차측량수거집영사도일개허의적전감기망락중,연후채용구유감입식긴치려파공능적DKF대영사수거진행려파예처리.재차기출상,장예처리결과전환위용우훈련신경망락적수입수거화수출수거,연후채용BPNN변식정전타라표이.실험표명,해방법가유효용우타라표이적모형변식.
By combining the distributed Kalman filter (DKF) with the back propagation neural network (BPNN),a novel method is proposed to identify the bias of electrostatic suspended gyroscope (ESG).Firstly,the data sets of into a sensor network,and DKF with embedded consensus filters is then used to preprocess the data sets.After transforming the preprocessed results into the trained input and the desired output of neural network,BPNN with the learning rate and the momentum term is further utilized to identify the ESG bias.As demonstrated in the experiment,the proposed approach is effective for the model identification of the ESG bias.
