基于改进胶囊神经网络的示功图诊断模型

针对传统胶囊神经网络特征提取结构单一，模型参数量大以及动态路由算法中相似度衡量粗糙等问题，该文提出一种改进的胶囊神经网络。应用Fire Module模块，将网络中特征图通道数先进行压缩，再通过多尺度的卷积核提取特征信息，进而提升网络的特征提取能力和减少网络模型的参数。将Dropout思想引入胶囊神经网络来增加模型的多样性，并在动态路由结构中应用Tanimoto系数提高动态路由算法性能，加快模型收敛提高精度。为验证改进胶囊神经网络的有效性，将改进的胶囊神经网络与双卷积胶囊神经网络和传统卷积神经网络以及VGG网络模型进行对比。实验结果表明，改进的模型具有更高的准确率和更快的训练速度。

Indicator Diagram Diagnosis Model Based on Improved Capsule Neural Network

Aiming at the problems of the traditional capsule neural network feature extraction structure is single, the model parameter is large, and the similarity measurement in the dynamic routing algorithm is rough, an improved capsule neural network is proposed. Using the Fire Module module, the number of feature map channels in the network is first compressed, and then the feature information is extracted through the multi-scale convolution kernel, thereby improving the feature extraction capability of the network and reducing the parameters of the network model. The Dropout idea is introduced into the capsule neural network to increase the diversity of the model, and the Tanimoto coefficient is used in the dynamic routing structure to improve the performance of the dynamic routing algorithm, speed up the model convergence and improve the accuracy. In order to verify the effectiveness of the improved capsule neural network, the improved capsule neural network is compared with the double convolutional capsule neural network, the traditional convolutional neural network and the VGG network model. Experimental results show that the improved model has higher accuracy and faster training speed.