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

@article{Amin2008AnalysisOP,
  title={Analysis of Pattern Recognition Algorithms Using Associative Memory Approach: A Comparative Study between the Hopfield Network and Distributed Hierarchical Graph Neuron (DHGN)},
  author={A.H.M. Amin and R.A.R. Mahmood and A. I. Khan},
  journal={2008 IEEE 8th International Conference on Computer and Information Technology Workshops},
  year={2008},
  pages={153-158}
}
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. 

References

Publications referenced by this paper.
Showing 1-10 of 17 references

An on-line scheme for threat detection within mobile ad hoc networks

  • A. I. Khan, A. H. Muhamad Amin
  • L. T. Yang, A. B. Waluyo, J. Ma, L. Tan, and B…
  • 2008
3 Excerpts

A peer-to-peer associative memory network for intelligent information systems

  • A. I. Khan
  • The Proceedings of The Thirteenth Australasian…
  • 2002
1 Excerpt

and A

  • K. J. Schultz, G.F.R. Gibson, F. Shafai
  • G. Bluschke. Content addressable memory vol…
  • 1999
1 Excerpt

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