本文選題：模擬電路 + 特征提取　； 參考：《湖南師范大學(xué)》2015年碩士論文

【摘要】：在高度發(fā)達(dá)的工業(yè)化進(jìn)程中,為了滿足安全性和環(huán)境的要求,對(duì)電路的測(cè)試要求也日益增高。隨著國際間的競爭越來越激烈,電路的則試技術(shù)也日益發(fā)展,如何對(duì)電路測(cè)試的數(shù)據(jù)進(jìn)行有效提取特征值和分類是目前工業(yè)發(fā)展中最關(guān)心的問題。當(dāng)前基于獨(dú)立成分分析(Independent component analysis,ICA)與支持向量機(jī)(support vector machine,SVM)勺故障診斷測(cè)試方法可以有效的解決上述問題。它的優(yōu)點(diǎn)在于不需要有精確的先驗(yàn)知識(shí),也不需要去構(gòu)建復(fù)雜的數(shù)學(xué)模型,僅僅利用在線或者離線的數(shù)據(jù),對(duì)數(shù)據(jù)之間的那種關(guān)系進(jìn)行挖掘,最終獲取電路的工作狀態(tài),完成系統(tǒng)的模擬故障診斷和檢測(cè)。獨(dú)立成分分析方法屬于數(shù)據(jù)驅(qū)動(dòng)的故障診斷和檢測(cè)的方法之一,是一種基于多變量的高階統(tǒng)計(jì)過程控制方法,它可以更有效的提取特征值;支持向量機(jī)可以更好的解決小樣本、高維數(shù)、非線性等問題。因此獨(dú)立成分分析方法結(jié)合支持向量機(jī)技術(shù)在電路測(cè)試、模擬故障診斷等領(lǐng)域都有重大的應(yīng)用價(jià)值。本文的主要研究工作如下：1、針對(duì)模擬故障診斷過程中的數(shù)據(jù)具有非高斯性的特點(diǎn),深入研究了自適應(yīng)核函數(shù)的獨(dú)立成分分析。對(duì)自適應(yīng)核函數(shù)的獨(dú)立成分分析從理論和實(shí)踐上做了詳細(xì)的分析和描述,并且對(duì)比了傳統(tǒng)的基于nfomax ICA和主元分析(PCA)在實(shí)驗(yàn)的精度和所發(fā)時(shí)間進(jìn)行驗(yàn)證,得出自適應(yīng)獨(dú)立成分分析方法是有效可行的。2、針對(duì)單一的高斯核函數(shù)是局部函數(shù),學(xué)習(xí)能力強(qiáng)但泛化性比較差;多層感知機(jī)核函數(shù)是全局函數(shù),泛化性強(qiáng)但學(xué)習(xí)能力比較弱�？紤]系統(tǒng)不同場合下的需求,本文將結(jié)合兩類核函數(shù)的優(yōu)點(diǎn),考慮模擬電路測(cè)試信號(hào)特征,構(gòu)造自適應(yīng)核函數(shù)。對(duì)自適應(yīng)的獨(dú)立成分分析方法處理后的數(shù)據(jù),用自適應(yīng)核函數(shù)支持向量機(jī)的方法進(jìn)行分類,能更好的解決高維數(shù)、小樣本、非線性等問題,最后本文用高斯核函數(shù)和多層感知機(jī)核函數(shù)去處理模擬電路故障,且從樣本訓(xùn)練時(shí)間和測(cè)試精度上進(jìn)行對(duì)比。3、把自適應(yīng)的非高斯獨(dú)立成分分析及支持向量機(jī)方法的具體診斷步驟進(jìn)行詳細(xì)說明,并且用S alley-key的帶通濾波和差動(dòng)放大電路以及手機(jī)測(cè)試中所產(chǎn)生的數(shù)據(jù)驗(yàn)證了該方法的可行性。
[Abstract]:In the process of highly developed industrialization, in order to meet the requirements of safety and environment, the requirements of circuit testing are also increasing day by day. With the international competition becoming more and more fierce, the trial technology of circuit is developing day by day. How to extract the characteristic value and classify the data of circuit test effectively is the most concerned problem in the current industrial development. The current fault diagnosis and testing methods based on Independent component Analysis (ICA) and support Vector Machine (SVM) can effectively solve the above problems. Its advantage is that it does not need to have accurate prior knowledge, does not need to build a complex mathematical model, only use online or offline data to mine the relationship between the data, and finally obtain the working state of the circuit. Complete system simulation fault diagnosis and detection. Independent component Analysis (ICA), one of the data-driven fault diagnosis and detection methods, is a multivariable based high-order statistical process control method, which can extract eigenvalues more effectively, and support vector machine can better solve small samples. Problems such as high dimension, nonlinearity, etc. Therefore, independent component analysis (ICA) combined with support vector machine (SVM) technology has great application value in circuit testing, analog fault diagnosis and so on. The main work of this paper is as follows: 1. The independent component analysis (ICA) of adaptive kernel function is studied in detail in view of the non-Gao Si property of the data in the process of simulating fault diagnosis. The independent component analysis of adaptive kernel function is analyzed and described in detail in theory and practice, and the accuracy and time of experiment based on nfomax and principal component analysis are compared. It is concluded that the adaptive independent component analysis method is effective and feasible. For a single Gao Si kernel function is a local function, the learning ability is strong but the generalization is poor, and the multi-layer perceptron kernel function is a global function with strong generalization but weak learning ability. Considering the requirements of the system in different situations, this paper will combine the advantages of two kinds of kernel functions, consider the characteristics of analog circuit test signals, and construct adaptive kernel functions. For the data processed by the adaptive independent component analysis (ICA) method, the method of adaptive kernel support vector machine can be used to classify the data, which can better solve the problems of high dimension, small sample, nonlinearity and so on. Finally, the Gao Si kernel function and the multi-layer perceptron kernel function are used to deal with the analog circuit fault. And compared with the sample training time and test accuracy, the adaptive non-Gao Si independent component analysis and the specific diagnostic steps of support vector machine method are explained in detail. The feasibility of the method is verified by using the bandpass filter and differential amplifier of S alley-key and the data generated in the mobile phone test.
【學(xué)位授予單位】：湖南師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TN710;TP18

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