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Neural Computing and Applications

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01.12.2013 | Original Article

Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks

verfasst von: Elrasheed Ismail Mohommoud Zayid, Mehmet Fatih Akay

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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Abstract

In this paper, we develop multi-layer feed-forward artificial neural network (MFANN) models for predicting the performance measures of a message-passing multiprocessor architecture interconnected by the simultaneous optical multiprocessor exchange bus (SOME-Bus), which is a fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the training and testing datasets. The performance of the MFANN prediction models is evaluated using standard error of estimate (SEE) and multiple correlation coefficient (R). Also, the results of the MFANN models are compared with the ones obtained by generalized regression neural network (GRNN), support vector regression (SVR), and multiple linear regression (MLR). It is shown that MFANN models perform better (i.e., lower SEE and higher R) than GRNN-based, SVR-based, and MLR-based models for predicting the performance measures of a message-passing multiprocessor architecture.

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Titel: Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks
verfasst von: Elrasheed Ismail Mohommoud Zayid
Mehmet Fatih Akay
Publikationsdatum: 01.12.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1267-9

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