Structural synthesis of fast two-layer neural networks

被引：1
|
作者：
Dorogov, AY [1 ]
机构：
[1] St Petersburg State Electrotech Univ, St Petersburg, Russia
来源：
CYBERNETICS AND SYSTEMS ANALYSIS | 2000年 / 36卷 / 04期
关键词：
neural networks; two-layer neural networks; fast neural networks (FNNs); dense neural networks; one-rank networks; fast two-layer neural networks; number of degrees of freedom of neural networks; plasticity (trainability) of neural networks; structural synthesis of fast two-layer neural networks;
D O I：
10.1007/BF02667059
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
Methods of construction of structural models of fast two-layer neural networks are considered. The methods are based on the criteria of minimum computing operations and maximum degrees of freedom. Optimal structural models of two-layer neural networks are constructed. Illustrative examples are given.
引用
收藏
页码：512 / 519
页数：8
相关论文
共 50 条
  • [21] l1 Regularization in Two-Layer Neural Networks
    Li, Gen
    Gu, Yuantao
    Ding, Jie
    IEEE SIGNAL PROCESSING LETTERS, 2022, 29 : 135 - 139
  • [22] A mean field view of the landscape of two-layer neural networks
    Mei, Song
    Montanari, Andrea
    Phan-Minh Nguyen
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2018, 115 (33) : E7665 - E7671
  • [23] A two-layer neural circuit controls fast forward locomotion in Drosophila
    Zhao, Qianhui
    Li, Xinhang
    Wen, Jun
    He, Yinhui
    Zheng, Nenggan
    Li, Wenchang
    Cardona, Albert
    Gong, Zhefeng
    CURRENT BIOLOGY, 2024, 34 (15)
  • [24] Synchronizability of two-layer networks
    Mingming Xu
    Jin Zhou
    Jun-an Lu
    Xiaoqun Wu
    The European Physical Journal B, 2015, 88
  • [25] Synchronizability of two-layer networks
    Xu, Mingming
    Zhou, Jin
    Lu, Jun-an
    Wu, Xiaoqun
    EUROPEAN PHYSICAL JOURNAL B, 2015, 88 (09):
  • [26] Two-layer queueing networks
    Kino, I
    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF JAPAN, 1997, 40 (02) : 163 - 185
  • [27] On the Connection Between Learning Two-Layer Neural Networks and Tensor Decomposition
    Mondelli, Marco
    Montanari, Andrea
    22ND INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS, VOL 89, 2019, 89
  • [28] An online gradient method with momentum for two-layer feedforward neural networks
    Zhang, Naimin
    APPLIED MATHEMATICS AND COMPUTATION, 2009, 212 (02) : 488 - 498
  • [29] Convergence of a Gradient Algorithm with Penalty for Training Two-layer Neural Networks
    Shao, Hongmei
    Liu, Lijun
    Zheng, Gaofeng
    2009 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY, VOL 4, 2009, : 76 - +
  • [30] Convergence of gradient method with momentum for two-layer feedforward neural networks
    Zhang, NM
    Wu, W
    Zheng, GF
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 2006, 17 (02): : 522 - 525
12345