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BP neural network for time series analysis predicted that by entering the corresponding time-series data to predict the future, suitable for beginners on the BP neural network learning
Batch version of the back-propagation algorithm.
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The "option.s" file is an example "options-setting" file.
Methods for designing a maintenance simulation training system for certain kind of radio are introduced. Fault modeling method is used to establish the fault database. The system sets up some typical failures, follow the prompts trainers can locate the fault source and confirm the type to accomplish corresponding fault maintenance training. A training evaluation means is given to examining and evaluating the training performance. The system intuitively and vividly shows the fault maintenance process, it can not only be used in teaching, but also in daily maintenance training to efficiently improve the maintenance operation level. Graphical programming language LabVIEW is used to develop the system platform.
微弱信號(hào)檢測(cè)裝置
四川理工學(xué)院 劉鵬飛、梁天德、曾學(xué)明
摘要:
本設(shè)計(jì)以TI的Launch Pad為核心板,采用鎖相放大技術(shù)設(shè)計(jì)并制作了一套微弱信號(hào)檢測(cè)裝置,用以檢測(cè)在強(qiáng)噪聲背景下已知頻率微弱正弦波信號(hào)的幅度值,并在液晶屏上數(shù)字顯示出所測(cè)信號(hào)相應(yīng)的幅度值。實(shí)驗(yàn)結(jié)果顯示其抗干擾能力強(qiáng),測(cè)量精度高。
關(guān)鍵詞:強(qiáng)噪聲;微弱信號(hào);鎖相放大;Launch Pad
Abstract:
This design is based on the Launch Pad of TI core board, using a lock-in amplifier technique designed and produced a weak signal detection device, to measure the known frequency sine wave signal amplitude values of the weak in the high noise background, and shows the measured signal amplitude of the corresponding value in the liquid crystal screen. Test results showed that it has high accuracy and strong anti-jamming capability.
Keywords: weak signal detection; lock-in-amplifier; Launch Pad
1、引言
隨著現(xiàn)代科學(xué)技術(shù)的發(fā)展,在科研與生產(chǎn)過(guò)程中人們?cè)絹?lái)越需要從復(fù)雜高強(qiáng)度的噪聲中檢測(cè)出有用的微弱信號(hào),因此對(duì)微弱信號(hào)的檢測(cè)成為當(dāng)前科研的熱點(diǎn)。微弱信號(hào)并不意味著信號(hào)幅度小,而是指被噪聲淹沒(méi)的信號(hào),“微弱”也僅是相對(duì)于噪聲而言的。只有在有效抑制噪聲的條件下有選擇的放大微弱信號(hào)的幅度,才能提取出有用信號(hào)。微弱信號(hào)檢測(cè)技術(shù)的應(yīng)用相當(dāng)廣泛,在生物醫(yī)學(xué)、光學(xué)、電學(xué)、材料科學(xué)等相關(guān)領(lǐng)域顯得愈發(fā)重要。
2、方案論證
針對(duì)微弱信號(hào)的檢測(cè)的方法有很多,比如濾波法、取樣積分器、鎖相放大器等。下面就針對(duì)這幾種方法做一簡(jiǎn)要說(shuō)明。
方案一:濾波法。
在大部分的檢測(cè)儀器中都要用到濾波方法對(duì)模擬信號(hào)進(jìn)行一定的處理,例如隔離直流分量,改善信號(hào)波形,防止離散化時(shí)的波形混疊,克服噪聲的不利影響,提高信噪比等。常用的噪聲濾波器有:帶通、帶阻、高通、低通等。但是濾波方法檢測(cè)信號(hào)不能用于信號(hào)頻譜與噪聲頻譜重疊的情況,有其局限性。雖然可以對(duì)濾波器的通頻帶進(jìn)行調(diào)節(jié),但其噪聲抑制能力有限,同時(shí)其準(zhǔn)確性與穩(wěn)定性將大打折扣。