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一种基于能量人工神经元模型的自生长、自组织神经网络

班晓娟 刘浩 徐卓然

班晓娟, 刘浩, 徐卓然. 一种基于能量人工神经元模型的自生长、自组织神经网络. 自动化学报, 2011, 37(5): 615-622. doi: 10.3724/SP.J.1004.2011.00615
引用本文: 班晓娟, 刘浩, 徐卓然. 一种基于能量人工神经元模型的自生长、自组织神经网络. 自动化学报, 2011, 37(5): 615-622. doi: 10.3724/SP.J.1004.2011.00615
BAN Xiao-Juan, LIU Hao, XU Zhuo-Ran. An Energy Artificial Neuron Model Based Self-growing and Self-organizing Neural Network. ACTA AUTOMATICA SINICA, 2011, 37(5): 615-622. doi: 10.3724/SP.J.1004.2011.00615
Citation: BAN Xiao-Juan, LIU Hao, XU Zhuo-Ran. An Energy Artificial Neuron Model Based Self-growing and Self-organizing Neural Network. ACTA AUTOMATICA SINICA, 2011, 37(5): 615-622. doi: 10.3724/SP.J.1004.2011.00615

一种基于能量人工神经元模型的自生长、自组织神经网络

doi: 10.3724/SP.J.1004.2011.00615
详细信息
    通讯作者:

    刘浩

An Energy Artificial Neuron Model Based Self-growing and Self-organizing Neural Network

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    Corresponding author: LIU Hao
  • 摘要: 本文结合近年生物学中神经科学的发展, 针对神经胶质细胞对生物神经元网络的生长提供能量支持的特性, 将神经胶质细胞的能量模型引入到人工神经元的概念模型中, 提出了能量人工神经元(Energy artificial neuron, EAN)的概念模型,并给出了数学表述. 同时,在能量人工神经元模型的基础上, 实现了一种新型自生长、自组织人工神经元网络 (EAN based self-growing and self-organizing neural network, ESGSONN), ESGSONN将神经元中的能量、网络的熵增量及样本与神经元权值的相似度的竞争作为生长的条件, 并对最优生长点中的获胜神经元进行单位步长调整. ESGSONN实现了快速生长、精确的样本数据分布密度保持、死神经元少的特性. 本文使用经典的16种动物实验(Ritter and Kohonen, 1989)验证了ESGSONN的正确性,并通过同SOFM、GCS等自组织网络的对比实验验证ESGSONN网络的特性. 最后,本文对ESGSONN在高维空间中的本质进行了讨论.
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出版历程
  • 收稿日期:  2010-08-03
  • 修回日期:  2010-12-27
  • 刊出日期:  2011-05-20

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