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

Author

Amin, A. H Muhamad ; Mahmood, R. A Raja ; Khan, A.I.

Author_Institution

Clayton Sch. of IT, Monash Univ., Clayton, VIC

fYear

2008

fDate

8-11 July 2008

Firstpage

153

Lastpage

158

Abstract

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.

Keywords

Hopfield neural nets; computational complexity; content-addressable storage; pattern recognition; Hopfield network algorithm; associative memory; comparative analysis; computational complexity; distributed hierarchical graph neuron; pattern recognition algorithms; recall efficiency; Associative Memory; Distributed Hierarchical Graph Neuron; Hopfield Network; Pattern Recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Sydney, QLD

Print_ISBN

978-0-7695-3242-4

Electronic_ISBN

978-0-7695-3239-1

Type

conf

DOI

10.1109/CIT.2008.Workshops.65

Filename

4568495