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dc.date.accessioned 2004-05-12T21:12:03Z
dc.date.available 2004-05-12T03:00:00Z
dc.date.issued 1999-03
dc.identifier.uri http://hdl.handle.net/10915/9378
dc.description.abstract Fast response, storage efficiency, fault tolerance and graceful degradation in face of scarce or spurious inputs make neural networks appropiate tools for Intelligent Computer Systems. But on the other hand, learning algorithms for neural networks involve CPU intensive processing and consequently great effort hass been done to develop parallel implementation intended for a reduction of learning time. Looking at both sides of the coin, this paper shows firstly two alternatives to parallelise the learning process and then an apllication of neural networks to computing systems. On the parallel alternative distributed implementations to parallelise the learning process of neural networks using pattern partitioning approach. Under this approach weight changes are computed concurently, exchanged between system components and adjusted accordingly until the whole parallel learning process is completed. On the application side, some design and implementation insights to build a system where decision support for load distribution is based on a neural network device are shown. Incoming task allocation, as a previous step, is a fundamental service aiming for improving distributed system perfomance facilitating further dynamic load balancing. A neural network device inserted into the kernel of a distributed system as an intelligent dool, allows to achieve automatic allocation of execution requests under some predefinided perfomance criteria based on resource availability and incoming process requeriments. Perfomamnec results of the parallelised approach for learning of backpropagation neural networks, are shown. This include a comparison of recall and generalisation abilities to support parallelism. en
dc.format.extent 14 p. es
dc.language en es
dc.title A parallel approach for backpropagation learning of neural networks en
dc.type Articulo es
sedici.identifier.uri http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_01/a%20parallel.pdf es
sedici.identifier.issn 1666-6038 es
sedici.creator.person Crespo, María Liz es
sedici.creator.person Piccoli, María Fabiana es
sedici.creator.person Printista, Alicia Marcela es
sedici.creator.person Gallard, Raúl Hector es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Facultad de Informática es
sedici.subtype Articulo es
sedici.rights.license Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc/3.0/
sedici.description.peerReview peer-review es
sedici2003.identifier ARG-UNLP-ART-0000000244 es
sedici.relation.journalTitle Journal of Computer Science & Technology es
sedici.relation.journalVolumeAndIssue vol. 1, no. 1 es
sedici.subject.acmcss98 Neural nets es

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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)