本文關(guān)鍵詞： 汽車衡 稱重誤差補(bǔ)償 神經(jīng)網(wǎng)絡(luò) 約束條件 先驗(yàn)知識(shí)　出處：《湖南師范大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：汽車衡作為衡器的重要分支,具有稱重范圍廣、測(cè)量速度快、便于控制計(jì)算等優(yōu)點(diǎn),廣泛應(yīng)用于倉(cāng)儲(chǔ)貿(mào)易、交通運(yùn)輸、工礦企業(yè)等部門�，F(xiàn)有汽車衡受到偏載誤差與線性度誤差的影響,稱重結(jié)果準(zhǔn)確度有待提高；同時(shí),汽車衡稱重?cái)?shù)據(jù)獲取不易,稱重系統(tǒng)處于小樣本狀態(tài)。針對(duì)這些缺點(diǎn),在國(guó)家自然科學(xué)基金項(xiàng)目“大型衡器偏載誤差機(jī)理與多傳感器稱重融合方法研究”的支持下,本文開展汽車衡稱重誤差補(bǔ)償方法研究：利用汽車衡先驗(yàn)知識(shí),構(gòu)建一種基于偏導(dǎo)數(shù)約束與Lagrange乘子法神經(jīng)網(wǎng)絡(luò)(PD-LMNN)優(yōu)化的稱重融合方法,提高小樣本下神經(jīng)網(wǎng)絡(luò)的泛化能力,從而減少汽車衡的稱重誤差；建立以單片機(jī)MSP430F449為信息處理核心的汽車衡實(shí)驗(yàn)平臺(tái),通過(guò)實(shí)驗(yàn)平臺(tái)測(cè)試,驗(yàn)證了這種方法的有效性。本文主要進(jìn)行以下工作：首先,介紹了汽車衡基本情況及發(fā)展趨勢(shì)、汽車衡的構(gòu)成及工作原理,指出了現(xiàn)有汽車衡稱重誤差補(bǔ)償?shù)牟蛔?闡述本文工作的重點(diǎn)；其次,構(gòu)建了BP神經(jīng)網(wǎng)絡(luò)的汽車衡稱重誤差補(bǔ)償模型,通過(guò)傳統(tǒng)的利用數(shù)據(jù)樣本訓(xùn)練算法(DINN),對(duì)該模型進(jìn)行訓(xùn)練,指出了這種方法在小樣本情況下的不足；通過(guò)研究汽車衡輸入-輸出函數(shù)偏導(dǎo)數(shù),并以此作為先驗(yàn)知識(shí),構(gòu)建有約束條件的神經(jīng)網(wǎng)絡(luò),利用Lagrange乘子法構(gòu)造增廣拉格朗日函數(shù)作為神經(jīng)網(wǎng)絡(luò)的目標(biāo)函數(shù),彌補(bǔ)了傳統(tǒng)神經(jīng)網(wǎng)絡(luò)因訓(xùn)練樣本不足導(dǎo)致的泛化能力差的問(wèn)題,通過(guò)兩種算法對(duì)比仿真實(shí)驗(yàn),驗(yàn)證了PD-LMNN算法的優(yōu)越性；再次,以單片機(jī)MSP430F449為信息處理核心、24bit模／數(shù)轉(zhuǎn)換器CS5532為稱重?cái)?shù)據(jù)采集單元,搭建了最大量程為250kg、分度值為0.2kg的汽車衡稱重實(shí)驗(yàn)平臺(tái),給出了硬件電路與軟件設(shè)計(jì)流程圖；最后,根據(jù)非自動(dòng)秤通用檢定規(guī)程,對(duì)采用PD-LMNN方法的汽車衡稱重實(shí)驗(yàn)平臺(tái)進(jìn)行了偏載誤差、重復(fù)性誤差、示值誤差和鑒別力進(jìn)行現(xiàn)場(chǎng)測(cè)試,給出了測(cè)試結(jié)果。測(cè)試表明,在實(shí)驗(yàn)室條件下,該汽車衡稱重實(shí)驗(yàn)平臺(tái)的偏載誤差、重復(fù)性誤差、示值誤差和鑒別力指標(biāo)均達(dá)到了國(guó)家標(biāo)準(zhǔn)《JJG 555-1996非自動(dòng)秤通用檢定規(guī)程》Ⅲ級(jí)秤要求。
[Abstract]:As an important branch of weighing instrument, automobile scale has the advantages of wide weighing range, fast measuring speed, easy to control calculation and so on. It is widely used in warehousing, trade, transportation and so on. The existing automobile scale is affected by bias error and linearity error, and the accuracy of weighing result needs to be improved. At the same time, the vehicle weighing data is not easy to obtain, the weighing system is in a small sample state. Supported by the project of National Natural Science Foundation "Research on the Mechanism of bias error and Multi-sensor weighing Fusion method of large weighing instrument", this paper carries out the research on the compensation method of weighing error of automobile scale: using the prior knowledge of automobile weighing scale. A weighing fusion method based on partial derivative constraint and Lagrange multiplier neural network (PD-LMNN) optimization is proposed to improve the generalization ability of neural networks with small samples. In order to reduce the weighing error of the vehicle scale; A vehicle scale experiment platform with MSP430F449 as the core of information processing is established, and the validity of this method is verified by the test platform. The main work of this paper is as follows: first. This paper introduces the basic situation and development trend of the automobile scale, the composition and working principle of the vehicle scale, points out the deficiency of the existing vehicle weighing error compensation, and expounds the emphases of the work in this paper. Secondly, a BP neural network model of vehicle weighing error compensation is constructed, which is trained by the traditional data sample training algorithm. The shortcomings of this method in the case of small samples are pointed out. By studying the partial derivative of the input-output function of the vehicle scale and taking it as a priori knowledge, a constrained neural network is constructed. The Lagrange multiplier method is used to construct the augmented Lagrangian function as the objective function of the neural network, which makes up for the poor generalization ability of the traditional neural network caused by the lack of training samples. The superiority of PD-LMNN algorithm is verified by comparing the two algorithms with simulation experiments. Thirdly, a 24bit A / D converter (CS5532) is used as a weighing data acquisition unit with MSP430F449 as the core of information processing. The maximum measurement range is 250kg. The design flow chart of hardware circuit and software is given in this paper. Finally, according to the general verification regulation of non-automatic scale, the bias error, repeatability error, indication error and discriminant force of the vehicle weighing experiment platform based on PD-LMNN method are tested on the spot. The test results show that, under the laboratory conditions, the bias error and repeatability error of the vehicle weighing experimental platform are obtained. Both the indication error and the discriminant index meet the requirements of the national standard < JJG 555-1996 general verification regulation of non-automatic scale > class 鈪，

本文編號(hào)：1476623