Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10831
Title: Automatic programming methodologies for electronic hardware fault monitoring
Authors: Abraham, A
Grosan, C
Keywords: Computational intelligence;Decision trees;Electronic hardware;Fault monitoring;Genetic programming;Neural networks
Issue Date: 2006
Citation: Journal of Universal Computer Science, 12 (4): 408 - 431, (2006)
Abstract: This paper presents three variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modeling of electronic circuits can be best performed by the Stressor - susceptibility interaction model. A circuit or a system is considered to be failed once the stressor has exceeded the susceptibility limits. For on-line prediction, validated stressor vectors may be obtained by direct measurements or sensors, which after pre-processing and standardization are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm and classification and regression trees. The performance of the proposed method is evaluated by comparing the experiment results with the actual failure model values. The developed model reveals that GP could play an important role for future fault monitoring systems.
URI: http://www.jucs.org/doi?doi=10.3217/jucs-012-04-0408
http://bura.brunel.ac.uk/handle/2438/10831
DOI: http://dx.doi.org/10.3217/jucs-012-04-0408
ISSN: 0948-6968
Appears in Collections:Dept of Computer Science Research Papers

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