A parallel approach for backpropagation learning of neural networks
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 gr...
Guardado en:
| Autores principales: | , , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
1999
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/9378 http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_01/a%20parallel.pdf |
| Aporte de: |
| id |
I19-R120-10915-9378 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Ciencias Informáticas Neural nets |
| spellingShingle |
Ciencias Informáticas Neural nets Crespo, María Liz Piccoli, María Fabiana Printista, Alicia Marcela Gallard, Raúl Hector A parallel approach for backpropagation learning of neural networks |
| topic_facet |
Ciencias Informáticas Neural nets |
| description |
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. |
| format |
Articulo Articulo |
| author |
Crespo, María Liz Piccoli, María Fabiana Printista, Alicia Marcela Gallard, Raúl Hector |
| author_facet |
Crespo, María Liz Piccoli, María Fabiana Printista, Alicia Marcela Gallard, Raúl Hector |
| author_sort |
Crespo, María Liz |
| title |
A parallel approach for backpropagation learning of neural networks |
| title_short |
A parallel approach for backpropagation learning of neural networks |
| title_full |
A parallel approach for backpropagation learning of neural networks |
| title_fullStr |
A parallel approach for backpropagation learning of neural networks |
| title_full_unstemmed |
A parallel approach for backpropagation learning of neural networks |
| title_sort |
parallel approach for backpropagation learning of neural networks |
| publishDate |
1999 |
| url |
http://sedici.unlp.edu.ar/handle/10915/9378 http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_01/a%20parallel.pdf |
| work_keys_str_mv |
AT crespomarializ aparallelapproachforbackpropagationlearningofneuralnetworks AT piccolimariafabiana aparallelapproachforbackpropagationlearningofneuralnetworks AT printistaaliciamarcela aparallelapproachforbackpropagationlearningofneuralnetworks AT gallardraulhector aparallelapproachforbackpropagationlearningofneuralnetworks AT crespomarializ parallelapproachforbackpropagationlearningofneuralnetworks AT piccolimariafabiana parallelapproachforbackpropagationlearningofneuralnetworks AT printistaaliciamarcela parallelapproachforbackpropagationlearningofneuralnetworks AT gallardraulhector parallelapproachforbackpropagationlearningofneuralnetworks |
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Repositorios |
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