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Research on a New Convolutional Neural Network Model Combined With Random Edges Adding

Author

Listed:
  • Jin Zhang

    (College of Information Science and Engineering, Hunan Normal University, China)

  • Sen Tian

    (Mathematics and Statistics, Hunan Normal University, China)

  • XuanYu Shu

    (Mathematics and Statistics, Hunan Normal University, China)

  • Sheng Chen

    (College of Information Science and Engineering, Hunan Normal University, China)

  • LingYu Chen

    (College of Information Science and Engineering, Hunan Normal University, China)

Abstract

It is always a hot and difficult point to improve the accuracy of the convolutional neural network model and speed up its convergence. Based on the idea of the small world network, a random edge adding algorithm is proposed to improve the performance of the 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 the 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 a probability of p = 0.1.

Suggested Citation

  • Jin Zhang & Sen Tian & XuanYu Shu & Sheng Chen & LingYu Chen, 2021. "Research on a New Convolutional Neural Network Model Combined With Random Edges Adding," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 12(1), pages 67-76, January.
    • Handle: RePEc:igg:jdst00:v:12:y:2021:i:1:p:67-76
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