论文标题
对新的卷积神经网络模型的研究以及随机边缘的添加
Research on a New Convolutional Neural Network Model Combined with Random Edges Adding
论文作者
论文摘要
提高卷积神经网络模型的准确性并加快其收敛态度始终是一个热门和困难的点。基于小世界网络的思想，提出了一种随机添加算法来提高卷积神经网络模型的性能。该算法将卷积神经网络模型作为基准测试，并以概率P随机将向后和跨层连接随机形成新的卷积神经网络模型。提出的想法可以通过更改卷积神经网络的拓扑结构来优化跨层连接，并为改进模型提供了新的想法。基于Fashion-Minst和CIFAR10数据集的仿真结果表明，通过随机边缘添加具有APROBOBIES P = 0.1的重建模型，大大提高了模型识别精度和训练收敛速度。
It is always a hot and difficult point to improve the accuracy of convolutional neural network model and speed up its convergence. Based on the idea of small world network, a random edge adding algorithm is proposed to improve the performance of convolutional neural network model. This algorithm takes the convolutional neural network model as a benchmark, and randomizes backwards and cross-layer connections with probability p to form a new convolutional neural network model. The proposed idea can optimize the cross layer connectivity by changing the topological structure of convolutional neural network, and provide a new idea for the improvement of the model. The simulation results based on Fashion-MINST and cifar10 data set show that the model recognition accuracy and training convergence speed are greatly improved by random edge adding reconstructed models with aprobability p = 0.1.
