提高自组织神经网络的容错性

[Proceedings] 1991 IEEE International Joint Conference on Neural Networks Pub Date : 1991-11-18 DOI:10.1109/IJCNN.1991.170279

F. Sha, Q. Gan

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引用次数: 0

摘要

将G.A. Carpenter和S. Grossberg(1987, 1988)引入的神经网络与Hopfield联想记忆(HAM)相结合，提出了一种混合神经网络(HNN)。HAM减少了样本中的噪声，并提供了ART1样本作为输入。为了使HAM的容量与ART1的容量匹配，还引入了一种新的召回算法(NHAM)来扩大HAM的容量。在NHAM和HNN的基础上，提出了HNN的改版(RHNN)。RHNN和HNN的区别在于RHNN有反馈回路，而HNN只有前馈路径。RHNN中的ART1为HAM提供信息来回忆记忆。计算机仿真表明，RHNN具有几个优点。

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Improving error tolerance of self-organizing neural nets

A hybrid neural net (HNN) combining the network introduced by G.A. Carpenter and S. Grossberg (1987, 1988) and the Hopfield associative memory (HAM) is developed. HAM diminishes noise in samples and provides ART1 samples as inputs. In order to match the capacity of HAM with that of ART1, a new recalling algorithm (NHAM) is also introduced to enlarge the capacity of HAM. Based on NHAM and HNN, a revised version of HNN (RHNN) is introduced. The difference between RHNN and HNN is that RHNN has feedback loops, while HNN has only feedforward paths. The ART1 in RHNN supplies information for HAM to recall memories. Computer simulation demonstrated that RHNN has several advantages.<>

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[Proceedings] 1991 IEEE International Joint Conference on Neural Networks

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