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Article . 2002 . Peer-reviewed
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Parallel design and implementation of SOM neural computing model in PVM environment of a distributed system

descriptionPublicationkeyboard_double_arrow_right Article , Conference object , Other literature type 22 Nov 2002Publisher:IEEE Comput. Soc. PressJournal:Proceedings. Advances in Parallel and Distributed Computing
Authors: Huiwei Guan; To-Yat Cheung; Chi-Kwong Li; Songnian Yu;

doi: 10.1109/apdc.1997.574010

Parallel design and implementation of SOM neural computing model in PVM environment of a distributed system

Abstract

A parallel design and implementation of the Self-Organizing Map (SOM) neural computing model is proposed. The parallel design of SOM is implemented in a parallel virtual machine (PVM) environment of a distributed system. A practical realization of SOM algorithm is investigated, the construction of computing module in parallel virtual machine is discussed, the communication methods and an optimization of message passing between multiple processes are proposed, and the parallel programming technique and a PVM implementation of SOM neural computing model are given and discussed in detail.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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