【摘要】：卷積神經(jīng)網(wǎng)絡被廣泛應用在圖像處理領域,不同算法對網(wǎng)絡識別率有較大的影響�；诖�,引入小波分解理論,通過BP傳播算法以及空間向量理論證明得到,相互獨立的特征更能表達原圖像的信息。通過小波分解去除卷積核之間的相關性,用較少的卷積核提取圖像更獨立、全面的特征,以提高網(wǎng)絡的識別性能。在MNIST、CIFAR-10和CK標準數(shù)據(jù)庫上進行分類識別實驗,實驗結果表明,此算法能在不同核函數(shù)尺寸的條件下取得較高識別率,且達到與傳統(tǒng)算法相同識別率的前提下,所需的訓練迭代次數(shù)更少,訓練時間更短。最后,將該算法應用到絕緣子故障識別中,并取得了良好的效果。
[Abstract]:Convolution neural network is widely used in image processing. Different algorithms have great influence on the recognition rate of the network. Based on this, the wavelet decomposition theory is introduced, and it is proved by BP propagation algorithm and spatial vector theory that the independent features can better express the information of the original image. The correlation between convolution kernels is removed by wavelet decomposition, and the image is extracted with fewer convolution kernels to extract more independent and comprehensive features in order to improve the recognition performance of the network. The classification and recognition experiments are carried out on MNIST,CIFAR-10 and CK standard databases. The experimental results show that the algorithm can achieve high recognition rate under the condition of different kernel function sizes, and achieve the same recognition rate as the traditional algorithm. The number of training iterations is less and the training time is shorter. Finally, the algorithm is applied to insulator fault identification, and good results are obtained.
【作者單位】： 華北電力大學電氣與電子工程學院;
【分類號】：TM216

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