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Shortest path routing algorithm using Hopfield neural network

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Abstract
A near-optimal routing algorithm employing a modified Hopfield neural network (HNN) is presented. Since it uses every piece of information that is available at the peripheral neurons, in addition to the highly correlated information that is available at the local neuron, faster convergence and better route optimality is achieved than with existing algorithms that employ the HNN. Furthermore, all the results are relatively independent of network topology for almost all source-destination pairs.
Author(s)
Ahn, Chang WookRamakrishna, Rudrapatna SubramanyamKang, C.G.Choi, I.C.
Issued Date
2001-09
Type
Article
DOI
10.1049/el:20010800
URI
https://scholar.gist.ac.kr/handle/local/18539
Publisher
Institute of Electrical Engineers
Citation
Electronics Letters, v.37, no.19, pp.1176 - 1178
ISSN
0013-5194
Appears in Collections:
Department of AI Convergence > 1. Journal Articles
Graduate School of AI Policy and Strategy > 1. Journal Articles
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