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In this paper, a convolutional neural network parameter training method based on Hausdorff difference is proposed to solve the problems of gradient vanishing and local optimum in the momentum algorithm. A momentum algorithm is proposed based on Hausdorff difference, introduced similar to the conception of Hausdorff derivative. Furthermore, the proposed algorithm is improved by using two new methods for adaptive nonlinear adjustment of the order. We analyse the influence of the order on the training results of the network parameters and verify the effectiveness of the proposed methods in improving the recognition accuracy and convergence speed of convolutional neural networks by the fashion-MNIST dataset and the CIFAR-10 dataset.

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Convolutional Neural Networks Parameter Training for SCM Algorithm Based on Hausdorff Difference

  • 会议论文

  • 发表日期：

    2025年

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  • Jing Jian,
  • Zhe Gao,
  • Jianyu Xiao,
  • Haibin Zhang

摘要

In this paper, a convolutional neural network parameter training method based on Hausdorff difference is proposed to solve the problems of gradient vanishing and local optimum in the momentum algorithm. A momentum algorithm is proposed based on Hausdorff difference, introduced similar to the conception of Hausdorff derivative. Furthermore, the proposed algorithm is improved by using two new methods for adaptive nonlinear adjustment of the order. We analyse the influence of the order on the training results of the network parameters and verify the effectiveness of the proposed methods in improving the recognition accuracy and convergence speed of convolutional neural networks by the fashion-MNIST dataset and the CIFAR-10 dataset.