本文選題：模擬電路　切入點(diǎn)：故障診斷　出處：《湖南師范大學(xué)》2016年碩士論文

【摘要】：隨著電子電路的發(fā)展,電路集成度的不斷提高,人們對(duì)模擬電路故障診斷提出了更高的要求。但由于模擬電路本身具有容差、非線性和故障現(xiàn)象多樣性等特點(diǎn),使得模擬電路故障診斷成為電路發(fā)展的熱點(diǎn)和難點(diǎn)。在模擬電路故障診斷中,故障特征向量的提取和故障模式識(shí)別是研究的重點(diǎn)與難點(diǎn)。但模式識(shí)別過程中獲得的原始數(shù)據(jù)往往包含大量的冗余信息,會(huì)影響故障診斷的效率和準(zhǔn)確率。因此,模擬電路故障診斷的一個(gè)關(guān)鍵點(diǎn)是如何有效地提取模擬電路的特征向量。本文以模擬電路故障特征向量提取為出發(fā)點(diǎn),研究了基于Treelet變換和混沌神經(jīng)網(wǎng)絡(luò)的模擬電路故障診斷方法。主要工作如下:(1)介紹了小波分析和層次聚類等故障特征提取方法。重點(diǎn)研究了一種新的模擬電路故障特征提取方法即Treelet變換。Treelet變換是一種將PCA、小波分析和層次聚類結(jié)合在一起的自適應(yīng)的多尺度的數(shù)據(jù)分析方法,特別適用于高維數(shù)據(jù)的降維。通過對(duì)這幾種特征提取方法進(jìn)行研究和對(duì)比,證明基于Treelet變換的模擬電路故障診斷率相比其他方法要高。(2)介紹了人工神經(jīng)網(wǎng)絡(luò)的原理及應(yīng)用,對(duì)BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)、學(xué)習(xí)算法進(jìn)行了研究,針對(duì)BP神經(jīng)網(wǎng)絡(luò)收斂速度慢、容易限于局部最小的特點(diǎn),提出了混沌神經(jīng)網(wǎng)絡(luò),利用混沌的特性構(gòu)造神經(jīng)網(wǎng)絡(luò),使神經(jīng)網(wǎng)絡(luò)具有混沌特性,優(yōu)化網(wǎng)絡(luò)結(jié)構(gòu)。(3)將Treelet變換與混沌神經(jīng)網(wǎng)絡(luò)結(jié)合應(yīng)用于模擬電路故障診斷,故障診斷結(jié)果與BP神經(jīng)網(wǎng)絡(luò)和小波神經(jīng)網(wǎng)絡(luò)方法相比,本文方法在模擬電路故障診斷中比BP網(wǎng)絡(luò)、小波神經(jīng)網(wǎng)絡(luò)診斷精度更高,收斂速度更快。
[Abstract]:With the development of electronic circuits and the continuous improvement of circuit integration, people put forward higher requirements for analog circuit fault diagnosis. However, the analog circuit itself has the characteristics of tolerance, nonlinearity and variety of fault phenomena, etc. In the analog circuit fault diagnosis, the analog circuit fault diagnosis has become a hot and difficult point in the development of the circuit. Fault feature vector extraction and fault pattern recognition are the key and difficult points in the research, but the original data obtained in the pattern recognition process often contain a lot of redundant information, which will affect the efficiency and accuracy of fault diagnosis. A key point of analog circuit fault diagnosis is how to extract the eigenvector of analog circuit effectively. The fault diagnosis method of analog circuit based on Treelet transform and chaotic neural network is studied. The main work is as follows: 1) the methods of fault feature extraction such as wavelet analysis and hierarchical clustering are introduced. Obstacle feature extraction method, I. E. Treelet transform. Treelet transform, is an adaptive multi-scale data analysis method, which combines Treelet, wavelet analysis and hierarchical clustering. It is especially suitable for dimensionality reduction of high-dimensional data. By studying and comparing these feature extraction methods, it is proved that the fault diagnosis rate of analog circuits based on Treelet transform is higher than that of other methods.) the principle and application of artificial neural network are introduced. In this paper, the structure and learning algorithm of BP neural network are studied. In view of the slow convergence speed of BP neural network, which is easy to be limited to the local minimum, a chaotic neural network is proposed, and the neural network is constructed by using the characteristic of chaos. Treelet transform and chaotic neural network are combined in analog circuit fault diagnosis. The fault diagnosis results are compared with BP neural network and wavelet neural network method. In the fault diagnosis of analog circuit, the wavelet neural network has higher diagnostic accuracy and faster convergence speed than BP neural network.
【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】：2016
【分類號(hào)】：TN710

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本文編號(hào)：1693073