發(fā)布時間：2018-06-02 14:35

  本文選題：加速度表參數(shù) + NAR神經(jīng)網(wǎng)絡��； 參考：《西南科技大學》2016年碩士論文

【摘要】：加速度表廣泛應用于慣性導航系統(tǒng)中,加速度表精度的提高對提高慣性導航控制精度具有很高的重要性。為提高加速度表的精度,一方面可以進一步改進加速度表的設計和生產(chǎn)工藝,另一方面可以對加速度表的參數(shù)進行補償。經(jīng)研究發(fā)現(xiàn),加速度表參數(shù)的時間序列具有很強的非線性特點,使用傳統(tǒng)的線性模型,難以達到很高的建模精度。因此本論文提出對加速度表參數(shù)進行非線性時間序列建模,以提高建模和預測精度。本論文以石英撓性加速度表為研究對象,研究并設計了加速度表靜態(tài)參數(shù)的標定方法,編寫了加速度表數(shù)據(jù)采集軟件系統(tǒng),以獲得研究所需要的加速度表的零偏和標度因數(shù);由于加速度表參數(shù)的時間序列具有很強的非線性特點,提出了使用非線性時間序列進行建模。在非線性時間序列算法研究中,在傳統(tǒng)BP神經(jīng)網(wǎng)絡的基礎上增加延時環(huán)節(jié)使其具有記憶歷史數(shù)據(jù)的能力,建立了NAR神經(jīng)網(wǎng)絡對參數(shù)進行建模;在AR模型的基礎上加入小波神經(jīng)網(wǎng)絡,建立了小波神經(jīng)網(wǎng)絡與AR組合模型,該模型利用AR模型擬合其線性部分,用小波神經(jīng)網(wǎng)絡擬合其非線性部分,并對小波神經(jīng)網(wǎng)絡的算法進行改進,使得該模型具有收斂速度快,訓練效果好。由于各種模型的預測方法都有其獨特的信息特征和適用條件,本論文利用組合預測理論,吸收每種模型的優(yōu)點,提出使用改進型貝葉斯組合預測方法,對NAR神經(jīng)網(wǎng)絡和小波神經(jīng)網(wǎng)絡與AR組合模型的預測結果進行組合預測。利用該方法與傳統(tǒng)ARMA模型的預測結果進行對比表明組合預測效果較好,準確率較高。
[Abstract]:Speedup meter is widely used in inertial navigation system. It is very important to improve the accuracy of inertial navigation system by improving the precision of accelerometer. In order to improve the accuracy of the accelerometer, the design and production process of the accelerometer can be further improved on the one hand, and the parameters of the accelerometer can be compensated on the other hand. It is found that the time series of accelerometer parameters have strong nonlinear characteristics and it is difficult to achieve high modeling accuracy by using the traditional linear model. In order to improve the accuracy of modeling and prediction, nonlinear time series modeling of accelerometer parameters is proposed in this paper. This paper takes the quartz flexible accelerometer as the research object, studies and designs the calibration method of the static parameters of the accelerometer, compiles the data acquisition software system of the accelerometer, in order to obtain the zero bias and scale factor of the accelerometer needed by the research. Because the time series of accelerometer parameters have strong nonlinear characteristics, a nonlinear time series is proposed to model the model. In the research of nonlinear time series algorithm, the NAR neural network is established to model the parameters by adding the delay link to the traditional BP neural network so that it has the ability to memorize the historical data. The combination model of wavelet neural network and AR is established by adding wavelet neural network on the basis of AR model. The model uses AR model to fit its linear part, and wavelet neural network to fit its nonlinear part. The algorithm of wavelet neural network is improved, which makes the model convergent quickly and has good training effect. Because the forecasting methods of various models have their unique information characteristics and applicable conditions, this paper uses the combination forecasting theory to absorb the advantages of each model, and proposes an improved Bayesian combination forecasting method. The prediction results of NAR neural network and wavelet neural network combined with AR are predicted. The comparison between this method and the traditional ARMA model shows that the combined prediction is effective and accurate.
【學位授予單位】：西南科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP212
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本文編號：1969113