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双向联想记忆神经网络抗噪声的能力

杜升之 陈增强 袁著祉 张兴会

杜升之, 陈增强, 袁著祉, 张兴会. 双向联想记忆神经网络抗噪声的能力. 自动化学报, 2005, 31(5): 668-674.
引用本文: 杜升之, 陈增强, 袁著祉, 张兴会. 双向联想记忆神经网络抗噪声的能力. 自动化学报, 2005, 31(5): 668-674.
DU Sheng-Zhi, CHEN Zeng-Qiang, YUAN Zhu-Zhi, ZHANG Xing-Hui. Anti-noise Capability of Bidirectional Associative Memory. ACTA AUTOMATICA SINICA, 2005, 31(5): 668-674.
Citation: DU Sheng-Zhi, CHEN Zeng-Qiang, YUAN Zhu-Zhi, ZHANG Xing-Hui. Anti-noise Capability of Bidirectional Associative Memory. ACTA AUTOMATICA SINICA, 2005, 31(5): 668-674.

双向联想记忆神经网络抗噪声的能力

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    通讯作者:

    杜升之

Anti-noise Capability of Bidirectional Associative Memory

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    Corresponding author: DU Sheng-Zhi
  • 摘要: This paper analyzes noise sensitivity of bidirectional association memory (BAM) and shows that the anti-noise capability of BAM relates not only to the minimum absolute value of net inputs(MAV), as some researchers found, but also to the variance of weights associated with synapse connections. In fact, it is determined by the quotient of these two factors. On this base, a novel learning algorithm—small variance leaning for BAM(SVBAM) is proposed, which is to decrease the variance of the weights of synapse matrix. Simulation experiments show that the algorithm can decrease the variance of weights efficiently, therefore, noise immunity of BAM is improved. At the same time, perfect recall of all training pattern pairs still can be guaranteed by the algorithm.
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出版历程
  • 收稿日期:  2004-04-14
  • 修回日期:  2005-04-08
  • 刊出日期:  2005-09-20

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