带有微分项改进的自适应梯度下降优化算法
Adaptive gradient descent optimization algorithm with improved differential term
摘要点击 134  全文点击 66  投稿时间:2021-01-18  修订日期:2021-05-19
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DOI编号  10.7641/CTA.2021.10061
  2022,39(4):623-632
中文关键词  卷积神经网络  梯度下降算法  微分项  权重更新  自适应学习率  悔界
英文关键词  convolutional neural networks  gradient descent algorithm  differential term  weight update  adaptive learning rate  regret bound
基金项目  中国航空科学基金项目(2019460T5001)资助.
作者单位邮编
葛泉波 同济大学 201804
张建朝 杭州电子科技大学 
杨秦敏 浙江大学 310027
李宏 中国飞行试验研究院 
中文摘要
      梯度下降算法作为卷积神经网络训练常用优化算法, 其性能的优劣直接影响网络训练收敛性. 本文主要分 析了目前梯度优化算法中存在超调而影响收敛性问题以及学习率自适应性问题, 提出了一种带微分项的自适应梯 度优化算法, 旨在改善网络优化过程收敛性的同时提高收敛速率. 首先, 针对优化过程存在较大超调量的问题, 通过 对迭代算法的重整合以及结合传统控制学原理引入微分项等方式来克服权重更新滞后于实际梯度改变的问题; 然 后, 引入自适应机制来应对因学习率的不适应性导致的收敛率差和收敛速率慢等问题; 紧接着, 基于柯西–施瓦茨和 杨氏不等式等证明了新算法的最差性能上界(悔界)为O(√T). 最后, 通过在包括MNIST数据集以及CIFAR–10基准 数据集上的仿真实验来验证新算法的有效性, 结果表明新算法引入的微分项和自适应机制的联合模式能够有效地 改善梯度下降算算法的收敛性能, 从而实现算法性能的明显改善.
英文摘要
      Gradient descent algorithms are common optimization algorithms for neural networks training, whose performance directly affects the convergence of the network training. This article mainly analyzes the problem of overshoots, which affects the convergence of the gradient optimization algorithm, and the problem of self adaptability of learning rate. An adaptive gradient optimization algorithm with differential term is proposed, which aims to improve the convergence rate of network optimization process as well as the convergence rate. Firstly, aiming at the problem of large overshoots in the optimization process, this article re-integrates the iterative algorithms by introducing the differential term combined with the traditional control theory, the shortcoming that the weight update lags behind the actual gradient change is overcome; Secondly, this article introduces an adaptive mechanism to improve the poor and slow convergence rate which are due to the unsuitable learning rate. Thirdly, this article proves that the regret bound O(√T) is achieved by using the Cauchy-Schwarz and Young’s inequalities. Finally, the effectiveness of the proposed method are verified by experimental tests on MNIST and CIFAR–10 benchmark datasets. The results show that the convergence performance of gradient descent methods can be improved significantly by introducing the differential term and adaptive mechanism, which can realize the significant improvement of optimization algorithms.