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

  更多相關(guān)文章： 測量不確定度 評估 ADC 測試方法 人工神經(jīng)網(wǎng)絡(luò)

【摘要】：ADC在數(shù)字化在計量測試儀器中起關(guān)鍵作用,它的準(zhǔn)確性能直接影響儀器的性能。ADC性能檢驗的重要方法之一就是對其響應(yīng)特性進行誤差分析,而分析誤差的現(xiàn)代手段就是進行不確定度評估,特別是對其動態(tài)性能進行不確定度評估。ADC的動態(tài)性能評估是目前測試和計量領(lǐng)域面臨的難題之一,為嘗試解決該問題,本文主要完成如下工作:(1)研究了測量不確定度的基本原理,分析了不同的測量不確定評估方法特點及其應(yīng)用范圍,提出可以利用神經(jīng)網(wǎng)絡(luò)算法進行ADC的動態(tài)性能的測量不確定度評估;(2)構(gòu)建了高速ADC的動態(tài)測試平臺,并對ADC的性能測試方法進行了比較和分析,包括基于直方圖的靜態(tài)測試方法和基于FFT的動態(tài)測試方法,最后選擇FFT測試方法對高速ADC進行動態(tài)測試;基于FFT研究了ADC的五個動態(tài)性能參數(shù):噪聲系數(shù)、信納比、有效位、總諧波失真和無雜散動態(tài)范圍,由此為ADC的動態(tài)性能的測量不確定度評估奠定基礎(chǔ);(3)在對高速ADC的動態(tài)測試研究過程中發(fā)現(xiàn)可以利用噪聲信號有效地提高ADC的轉(zhuǎn)換性能,從而提高ADC的轉(zhuǎn)換精度和抗干擾性;在測試中也獲得了ADC的最佳動態(tài)性能參數(shù),這些參數(shù)可以作為ADC動態(tài)性能的測量不確定度評估的來源;(4)針對ADC的動態(tài)測量不確定度評定這一難點問題,提出利用人工神經(jīng)網(wǎng)絡(luò)算法對ADC進行測量不確定度評估;為此建立了基于神經(jīng)網(wǎng)絡(luò)ADC測量不確定評估的數(shù)學(xué)模型,并基于該模型應(yīng)用MATLAB開發(fā)了ADC動態(tài)性能評估應(yīng)用程序;(5)為驗證上述模型的有效性,以ADI的AD6645-105為實例進行測量不確定度評定;為此,先應(yīng)用GUM中的A類和B類對其性能參數(shù)進行測量不確定度評估,再應(yīng)用基于神經(jīng)網(wǎng)絡(luò)的算法對其動態(tài)性能進行不確定度評估;之后對這兩種評估方法的結(jié)果進行對比分析,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ADC的動態(tài)性能參數(shù)進行評定。
[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)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN792

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本文編號：1399408