【摘要】 卷积神经网络(CNN)及其变体模型应用于图像分类技术,因其大量的训练参数导致CNN模型训练过于复杂,增加了成本开销,也易产生梯度消失或梯度爆炸问题。为此,提出滑动平均和规则决策的卷积神经网络模型,并将其应用于图像分类中。将特征映射层与感知器网络(MLP)层结合,利用滑动平均对网络层之间的权重参数进行调整,并对预测目标采用置信度规则策略实现决策优化,提升模型的泛化性能。试验结果表明:滑动平均和规则决策的卷积神经网络模型具有更好的鲁棒性和分类效果。更多还原

【Abstract】 Due to large number of training parameters when applying convolutional neural network(CNN) and its variant model to image classification, the training of them is too complicated to increase the cost and cause the problem of gradient disappearance or gradient explosion. A CNN with moving average and rule decision was proposed and applied to image classification. The feature mapping layer was combined with the multilayer perceptron(MLP) layer, the weight parameters between the network layers were adjusted using the moving average, and the prediction target was optimized using the confidence rule strategy to improve the generalization performance of the model. Experimental results show that the CNN model based on moving average and rule decision has better robustness and classification effect.更多还原