基于联想记忆方法的模式识别算法分析——Hopfield网络与分布式分层图神经元(DHGN)的比较研究

2008 IEEE 8th International Conference on Computer and Information Technology Workshops Pub Date : 2008-07-08 DOI:10.1109/CIT.2008.WORKSHOPS.65

A. Amin, R. Mahmood, A.I. Khan

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引用次数: 16

摘要

本文对两种基于联想记忆的模式识别算法进行了比较分析。我们将建立的Hopfield网络算法与我们的分布式分层图神经元(DHGN)算法进行了比较。讨论了这些算法的计算复杂度和召回效率。结果表明，与Hopfield网络相比，DHGN具有更低的计算复杂度和更高的召回效率。

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Analysis of Pattern Recognition Algorithms Using Associative Memory Approach: A Comparative Study between the Hopfield Network and Distributed Hierarchical Graph Neuron (DHGN)

In this paper, we conduct a comparative analysis of two associative memory-based pattern recognition algorithms. We compare the established Hopfield network algorithm with our novel Distributed Hierarchical Graph Neuron (DHGN) algorithm. The computational complexity and recall efficiency aspects of these algorithms are discussed. The results show that DHGN offers lower computational complexity with better recall efficiency compared to the Hopfield network.

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2008 IEEE 8th International Conference on Computer and Information Technology Workshops

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