本文關(guān)鍵詞：ADC的測(cè)量不確定度評(píng)估方法研究　出處：《陜西科技大學(xué)》2015年碩士論文　論文類(lèi)型：學(xué)位論文

  更多相關(guān)文章： 測(cè)量不確定度 評(píng)估 ADC 測(cè)試方法 人工神經(jīng)網(wǎng)絡(luò)

【摘要】：ADC在數(shù)字化在計(jì)量測(cè)試儀器中起關(guān)鍵作用,它的準(zhǔn)確性能直接影響儀器的性能。ADC性能檢驗(yàn)的重要方法之一就是對(duì)其響應(yīng)特性進(jìn)行誤差分析,而分析誤差的現(xiàn)代手段就是進(jìn)行不確定度評(píng)估,特別是對(duì)其動(dòng)態(tài)性能進(jìn)行不確定度評(píng)估。ADC的動(dòng)態(tài)性能評(píng)估是目前測(cè)試和計(jì)量領(lǐng)域面臨的難題之一,為嘗試解決該問(wèn)題,本文主要完成如下工作:(1)研究了測(cè)量不確定度的基本原理,分析了不同的測(cè)量不確定評(píng)估方法特點(diǎn)及其應(yīng)用范圍,提出可以利用神經(jīng)網(wǎng)絡(luò)算法進(jìn)行ADC的動(dòng)態(tài)性能的測(cè)量不確定度評(píng)估;(2)構(gòu)建了高速ADC的動(dòng)態(tài)測(cè)試平臺(tái),并對(duì)ADC的性能測(cè)試方法進(jìn)行了比較和分析,包括基于直方圖的靜態(tài)測(cè)試方法和基于FFT的動(dòng)態(tài)測(cè)試方法,最后選擇FFT測(cè)試方法對(duì)高速ADC進(jìn)行動(dòng)態(tài)測(cè)試;基于FFT研究了ADC的五個(gè)動(dòng)態(tài)性能參數(shù):噪聲系數(shù)、信納比、有效位、總諧波失真和無(wú)雜散動(dòng)態(tài)范圍,由此為ADC的動(dòng)態(tài)性能的測(cè)量不確定度評(píng)估奠定基礎(chǔ);(3)在對(duì)高速ADC的動(dòng)態(tài)測(cè)試研究過(guò)程中發(fā)現(xiàn)可以利用噪聲信號(hào)有效地提高ADC的轉(zhuǎn)換性能,從而提高ADC的轉(zhuǎn)換精度和抗干擾性;在測(cè)試中也獲得了ADC的最佳動(dòng)態(tài)性能參數(shù),這些參數(shù)可以作為ADC動(dòng)態(tài)性能的測(cè)量不確定度評(píng)估的來(lái)源;(4)針對(duì)ADC的動(dòng)態(tài)測(cè)量不確定度評(píng)定這一難點(diǎn)問(wèn)題,提出利用人工神經(jīng)網(wǎng)絡(luò)算法對(duì)ADC進(jìn)行測(cè)量不確定度評(píng)估;為此建立了基于神經(jīng)網(wǎng)絡(luò)ADC測(cè)量不確定評(píng)估的數(shù)學(xué)模型,并基于該模型應(yīng)用MATLAB開(kāi)發(fā)了ADC動(dòng)態(tài)性能評(píng)估應(yīng)用程序;(5)為驗(yàn)證上述模型的有效性,以ADI的AD6645-105為實(shí)例進(jìn)行測(cè)量不確定度評(píng)定;為此,先應(yīng)用GUM中的A類(lèi)和B類(lèi)對(duì)其性能參數(shù)進(jìn)行測(cè)量不確定度評(píng)估,再應(yīng)用基于神經(jīng)網(wǎng)絡(luò)的算法對(duì)其動(dòng)態(tài)性能進(jìn)行不確定度評(píng)估;之后對(duì)這兩種評(píng)估方法的結(jié)果進(jìn)行對(duì)比分析,得到的結(jié)論是:應(yīng)用神經(jīng)網(wǎng)絡(luò)算法可以更快速而準(zhǔn)確的對(duì)ADC的動(dòng)態(tài)性能參數(shù)進(jìn)行評(píng)定。
[Abstract]:In the ADC in the digital measurement instruments play a key role, one of the important methods of accurate performance directly affects the performance of.ADC performance test instrument is its error analysis on the response characteristics of modern means of error analysis is the evaluation of uncertainty, especially the dynamic performance is the uncertainty evaluation of dynamic performance evaluation.ADC is one of the problems currently facing the test and measurement field, in order to try to solve this problem, this paper mainly completed the following work: (1) research on the measurement uncertainty of basic principle, analyzes the different measurement uncertainty evaluation method of characteristics and application range of measurement, put forward dynamic performance using ADC neural network algorithm the evaluation of uncertainty; (2) establishes a dynamic test platform of high speed ADC, and the ADC performance testing methods are compared and analyzed, including based on histogram The method of static testing and dynamic testing method based on FFT, the final choice of FFT test method of dynamic testing of high speed ADC; five dynamic performance parameters of FFT ADC were studied based on noise coefficient, SINAD, effective, total harmonic distortion and spurious free dynamic range, the dynamic performance of the ADC measurement uncertainty to lay the foundation of evaluation; (3) in the course of study on dynamic test of high speed ADC that can effectively improve the ADC conversion performance using the noise signal, so as to improve the anti-interference and the conversion accuracy of ADC; in the test also won the best dynamic performance parameters of the ADC, these parameters can be used to measure the dynamic performance of ADC no source of evaluation; (4) according to the uncertainty of the difficult problems in evaluation of dynamic measurement of ADC, ADC of the measurement uncertainty evaluation using artificial neural network algorithm is established based on this; Neural network ADC measurement uncertainty evaluation mathematical model, based on the model developed by MATLAB ADC dynamic performance evaluation of the application; (5) to verify the validity of the model, using ADI AD6645-105 for evaluation of measurement uncertainty for example; therefore, the first application of GUM A and B on its performance the parameters of the measurement uncertainty evaluation, and the application of neural network algorithm for uncertainty evaluation of the dynamic performance based on the two; after the assessment results were analyzed, the conclusion is: the application of neural network algorithm for the dynamic performance of the ADC parameter is more rapid and accurate assessment.

【學(xué)位授予單位】：陜西科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TN792

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