%0 Conference Paper %1 abraham:2005:CEC %A Abraham, Ajith %A Grosan, Crina %B Proceedings of the 2005 IEEE Congress on Evolutionary Computation %C Edinburgh, UK %D 2005 %E Corne, David %E Michalewicz, Zbigniew %E Dorigo, Marco %E Eiben, Gusz %E Fogel, David %E Fonseca, Carlos %E Greenwood, Garrison %E Chen, Tan Kay %E Raidl, Guenther %E Zalzala, Ali %E Lucas, Simon %E Paechter, Ben %E Willies, Jennifier %E Guervos, Juan J. Merelo %E Eberbach, Eugene %E McKay, Bob %E Channon, Alastair %E Tiwari, Ashutosh %E Volkert, L. Gwenn %E Ashlock, Dan %E Schoenauer, Marc %I IEEE Press %K ANN, LGP MEP, algorithms, genetic programming, %P 1563--1569 %T Genetic Programming Approach for Fault Modeling of Electronic Hardware %U http://www.softcomputing.net/cec05.pdf %V 2 %X presents two variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modelling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed 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 preprocessing and standardisation are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm. 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. %@ 0-7803-9363-5

@inproceedings{abraham:2005:CEC, abstract = {presents two variants of Genetic Programming (GP) approaches for intelligent online performance monitoring of electronic circuits and systems. Reliability modelling of electronic circuits can be best performed by the stressor - susceptibility interaction model. A circuit or a system is deemed 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 preprocessing and standardisation are fed into the GP models. Empirical results are compared with artificial neural networks trained using backpropagation algorithm. 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.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Edinburgh, UK}, author = {Abraham, Ajith and Grosan, Crina}, biburl = {https://www.bibsonomy.org/bibtex/24d31d0b350e38d98c505978625724a20/brazovayeye}, booktitle = {Proceedings of the 2005 IEEE Congress on Evolutionary Computation}, editor = {Corne, David and Michalewicz, Zbigniew and Dorigo, Marco and Eiben, Gusz and Fogel, David and Fonseca, Carlos and Greenwood, Garrison and Chen, Tan Kay and Raidl, Guenther and Zalzala, Ali and Lucas, Simon and Paechter, Ben and Willies, Jennifier and Guervos, Juan J. Merelo and Eberbach, Eugene and McKay, Bob and Channon, Alastair and Tiwari, Ashutosh and Volkert, L. Gwenn and Ashlock, Dan and Schoenauer, Marc}, interhash = {f03cc6e610ac5b214548ff49dea3ba28}, intrahash = {4d31d0b350e38d98c505978625724a20}, isbn = {0-7803-9363-5}, keywords = {ANN, LGP MEP, algorithms, genetic programming,}, month = {2-5 September}, notes = {CEC2005 - A joint meeting of the IEEE, the IEE, and the EPS.}, organisation = {IEEE Computational Intelligence Society, Institution of Electrical Engineers (IEE), Evolutionary Programming Society (EPS)}, pages = {1563--1569}, publisher = {IEEE Press}, publisher_address = {445 Hoes Lane, P.O. Box 1331, Piscataway, NJ 08855-1331, USA}, size = {7 pages}, timestamp = {2008-06-19T17:35:11.000+0200}, title = {Genetic Programming Approach for Fault Modeling of Electronic Hardware}, url = {http://www.softcomputing.net/cec05.pdf}, volume = 2, year = 2005 }