By D. Dasgupta, K. KrishnaKumar, D. Wong, M. Berry (auth.), Giuseppe Nicosia, Vincenzo Cutello, Peter J. Bentley, Jon Timmis (eds.)
Arti?cial Immune structures have come of age. they're now not an imprecise computersciencetechnique,workedonbyacoupleoffarsightedresearchgroups. this present day, researchers around the globe are engaged on new laptop algorithms encouraged by means of the workings of the immune method. This lively ?eld of analysis investigates how immunobiology might help our know-how, and alongside the way in which is starting to aid biologists comprehend their distinctive difficulties. AIS is now the right age to appreciate its roots, its context within the study neighborhood, and its interesting destiny. It has grown too substantial to be con?ned to s- cial classes in evolutionary computation meetings. AIS researchers at the moment are forming their very own neighborhood and identification. The foreign convention on Arti?cial Immune structures is proud to be the ideal convention within the quarter. As its organizers, we have been commemorated to have one of these number of cutting edge and unique scienti?c papers awarded this yr. ICARIS 2004 was once the 3rd foreign convention devoted completely to the ?eld of Arti?cial Immune platforms (AIS). It used to be held in Catania, at the attractive island of Sicily, Italy, in the course of September 13–16, 2004. whereas web hosting the convention, the town of Catania gave the individuals the chance to benefit from the richness of its old and cultural surroundings and the wonderful thing about its normal assets, the ocean, and the Etna volcano.
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Additional resources for Artificial Immune Systems: Third International Conference, ICARIS 2004, Catania, Sicily, Italy, September 13-16, 2004. Proceedings
17. 18. 19. 20. 21. 22. ,: New developments using AI in fault diagnosis. : Survey of model-based failure detection and isolation in complex plants. : Robust model-based fault diagnosis for dynamic systems. Kluwer Academic Publishers, Dordrechi. : Fault detection and diagnosis in engineering systems. : Fuzzy-model-based parity equations for fault isolation. : Validity index for crisp and fuzzy clusters. : Artificial Immune Systems and Their Applications. : Immunity-based systems: a survey. Computational Cybernetics and Simulation.
Conclusively, the proposed scheme could be expected to provide various feasible ideas for fault diagnosis applications. Acknowledgement. , for making this work possible with grant NSC90-2213-E036-011. -C. -Y. -C. Cheng References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. ,: New developments using AI in fault diagnosis. : Survey of model-based failure detection and isolation in complex plants. : Robust model-based fault diagnosis for dynamic systems. Kluwer Academic Publishers, Dordrechi.
Artificial Immune Regulation (AIR) for Model-Based Fault Diagnosis 33 If the process data are significantly noisy, it can lead to either false alarms or nondetection of fault. The filtered residual and fault alarm concentration (FAC) is adopted for fault detection. To reduce the effects of modeling errors and noise further, the evaluation of the filtered residual is formulated as where r(t) is the output residual. is a weighting parameter and The larger value of the longer the detection period became.