Interchangeability of learning rate and gain in backpropagation neural networks

被引：0
|
作者：
Thimm, Georg
Moerland, Perry
Fiesler, Emile
机构：
来源：
Neural Computation | 1996年 / 8卷 / 02期
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [31] Active training of backpropagation neural networks using the learning by experimentation methodology
    Lin, FR
    Shaw, MJ
    ANNALS OF OPERATIONS RESEARCH, 1997, 75 (0) : 105 - 122
  • [32] Mechanism for feature learning in neural networks and backpropagation-free machine learning models
    Radhakrishnan, Adityanarayanan
    Beaglehole, Daniel
    Pandit, Parthe
    Belkin, Mikhail
    SCIENCE, 2024, 383 (6690) : 1461 - 1467
  • [33] Incremental backpropagation learning networks
    Fu, LM
    Hsu, HH
    Principe, JC
    IEEE TRANSACTIONS ON NEURAL NETWORKS, 1996, 7 (03): : 757 - 761
  • [34] ANALYSIS OF THE ERROR BACKPROPAGATION LEARNING ALGORITHMS WITH GAIN
    JIA, Q
    HAGIWARA, K
    USUI, S
    TODA, N
    SYSTEMS AND COMPUTERS IN JAPAN, 1995, 26 (06) : 49 - 58
  • [35] Efficient training of backpropagation neural networks
    Otair, Mohammed A.
    Salameh, Walid A.
    NEURAL NETWORK WORLD, 2006, 16 (04) : 291 - 311
  • [36] On Chaos and Neural Networks: The Backpropagation Paradigm
    K. Bertels
    L. Neuberg
    S. Vassiliadis
    D.G. Pechanek
    Artificial Intelligence Review, 2001, 15 : 165 - 187
  • [37] Fuzzy backpropagation training of neural networks
    Gerke, M
    Hoyer, H
    COMPUTATIONAL INTELLIGENCE: THEORY AND APPLICATIONS, 1997, 1226 : 416 - 427
  • [38] On the overtraining phenomenon of backpropagation neural networks
    Tzafestas, SG
    Dalianis, PJ
    Anthopoulos, G
    MATHEMATICS AND COMPUTERS IN SIMULATION, 1996, 40 (5-6) : 507 - 521
  • [39] Spiking Neural Networks Using Backpropagation
    Syed, Tehreem
    Kakani, Vijay
    Cui, Xuenan
    Kim, Hakil
    2021 IEEE REGION 10 SYMPOSIUM (TENSYMP), 2021,
  • [40] On chaos and neural networks: The backpropagation paradigm
    Bertels, K
    Neuberg, L
    Vassiliadis, S
    Pechanek, DG
    ARTIFICIAL INTELLIGENCE REVIEW, 2001, 15 (03) : 165 - 187
12345