计算机与数字工程2023，Vol.51Issue(11)：2553-2556,4.DOI:10.3969/j.issn.1672-9722.2023.11.015
基于Adam优化的卷积神经网络随机共振现象研究
Research on Stochastic Resonance of Convolutional Neural Network Based on Adam Optimization
摘要
Abstract
In this paper,stochastic resonance is used to improve the performance of Adam optimized convolutional neural net-work under limited computational force.For the back-propagation algorithm,the momentum gradient descent algorithm is used to update the network parameters for Adam optimization,and the MNIST handwritten numeral set is used for simulation experiments.Under the experimental conditions of this paper,compared with the network of momentum gradient descent algorithm,the crossover entropy of Adam optimized network is reduced under the first 15 epochs.Increasing the number of training samples can reduce the reduction of cross entropy.This paper adds Gaussian noise to the output neurons of the convolution neural network optimized by Ad-am.The simulation results show that the stochastic resonance phenomenon occurs in the percentage of cross entropy reduction.In-creasing the number of training samples can reduce the effect of stochastic resonance phenomenon.关键词
梯度下降算法/Adam优化/高斯噪声/仿真实验Key words
gradient descent algorithm/Adam optimization/Gaussian noise/simulation experiment分类
信息技术与安全科学引用本文复制引用
尚天鹏,王友国..基于Adam优化的卷积神经网络随机共振现象研究[J].计算机与数字工程,2023,51(11):2553-2556,4.基金项目
国家自然科学基金项目(编号:62071248) （编号:62071248）
江苏省研究生科研创新计划(编号:KYCX20_0730)资助. （编号:KYCX20_0730）
