电子科技大学学报2018，Vol.47Issue(2)：230-234,5.DOI:10.3969/j.issn.1001-0548.2018.02.011
混合PSO优化卷积神经网络结构和参数
Optimizing Structure and Parameters of Convolutional Neural Networks Using Hybrid PSO
摘要
Abstract
In order to make convolutional neural network get optimal connection automatically without experienced guidance and improve the optimizing effectiveness for parameters of convolutional neural network, a new method using both particle swarm optimization algorithm and discrete particle swarm optimization algorithm is proposed to optimize parameters and feature maps connecting structure of convolutional neural network. The particle swarm optimization is applied to optimize the weights of convolutional neural network at first, and then the discrete particle swarm optimization is applied to optimize feature maps connections between sub-sampling layer and convolutional layer. The method is applied to MNIST database and CIFAR-10 database, compared to convolutional neural networks of other connecting structures and other recognition methods, results shown that this method can optimize the parameters and structure of the network effectively, accelerate network convergence and improve the recognition accuracy.关键词
卷积神经网络/离散粒子群优化/手写字符识别/粒子群优化/结构优化Key words
convolutional neural network/discrete particle swarm optimization/handwritten character recognition/particle swarm optimization/structural optimization分类
信息技术与安全科学引用本文复制引用
唐贤伦,刘庆,张娜,周家林..混合PSO优化卷积神经网络结构和参数[J].电子科技大学学报,2018,47(2):230-234,5.基金项目
国家自然科学基金(60905066) （60905066）
重庆市教委科学技术研究项目(KJ1500401) （KJ1500401）
