發(fā)布時(shí)間：2018-06-17 12:05

  本文選題：卷積結(jié)構(gòu) + 獨(dú)立主成分分析��； 參考：《南昌大學(xué)》2017年碩士論文

【摘要】：深度學(xué)習(xí)是目前研究的熱點(diǎn),本文基于前人的研究,針對(duì)傳統(tǒng)卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)算法在卷積核數(shù)量確定、卷積核參數(shù)初始化以及網(wǎng)絡(luò)層與層之間通道選擇不能夠自主學(xué)習(xí)的問(wèn)題,提出了基于獨(dú)立主成分分析和基于云模型的卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)訓(xùn)練方法。首先分析研究主成分分析的主向量與卷積神經(jīng)網(wǎng)絡(luò)卷積核數(shù)量之間的關(guān)系;然后運(yùn)用獨(dú)立主成分神經(jīng)網(wǎng)絡(luò)對(duì)無(wú)標(biāo)記圖像混合信號(hào)分析,確定深度卷積核數(shù)量,模仿快速固定算法對(duì)卷積核參數(shù)初始化;最后采用云模型選擇器對(duì)模型層與層之間通道連接進(jìn)行選擇,從而達(dá)到減少網(wǎng)絡(luò)訓(xùn)練步數(shù)的目的。實(shí)驗(yàn)結(jié)果表明通過(guò)主成分分析所得抽樣樣本的平均主向量個(gè)數(shù)近似模型在測(cè)試集準(zhǔn)確率基本不變的情況下卷積核數(shù)量的拐點(diǎn),減少了重復(fù)實(shí)驗(yàn)來(lái)確定卷積核數(shù)量的次數(shù)。獨(dú)立主成分析以及云模型選擇器使得模型訓(xùn)練步數(shù)減少,加快了模型收斂,所提算法要比經(jīng)典算法運(yùn)行速率上提高5.4%左右,測(cè)試集準(zhǔn)確率基本與原卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)基本持平。
[Abstract]:Deep learning is a hot topic at present. Based on the previous research, this paper proposes the problem that the number of convolution kernel is determined, the parameter of convolution kernel is initialized and the selection of the channel between layer and layer can not be autonomous learning. A convolution neural network based on independent principal component analysis and cloud model is proposed. The relationship between the principal vector of the principal component analysis and the convolution kernel number of the convolution neural network is analyzed. Then the independent principal component neural network is used to analyze the unmarked image mixed signal, to determine the number of the deep convolution kernel, and to initialize the convolution kernel parameters by the fast fixed algorithm. Finally, the cloud model is used to select the model. The selector chooses the channel connection between the model layer and the layer to reduce the number of network training steps. The experimental results show that the average principal vector number approximation model of the sample sample obtained by the principal component analysis reduces the volume of the volume of the kernel number under the condition that the accuracy of the test set is basically the same, and the repeated experiment is reduced to determine the volume. The number of kernel number, independent principal analysis and cloud model selector reduce the number of model training steps and accelerate the convergence of the model. The proposed algorithm is about 5.4% higher than the classical algorithm, and the accuracy of the test set is basically equal to the original convolution neural network structure.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP18

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