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Please use this identifier to cite or link to this item: https://doi.org/10.1109/FSKD.2009.137
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dc.titleDetecting automotive exhaust gas based on fuzzy inference system
dc.contributor.authorLi, S.-J.
dc.contributor.authorBai, M.
dc.contributor.authorWang, Q.
dc.contributor.authorChen, B.
dc.contributor.authorZhao, X.-B.
dc.contributor.authorYang, T.
dc.contributor.authorWang, Z.-X.
dc.date.accessioned2014-06-19T03:05:47Z
dc.date.available2014-06-19T03:05:47Z
dc.date.issued2009
dc.identifier.citationLi, S.-J., Bai, M., Wang, Q., Chen, B., Zhao, X.-B., Yang, T., Wang, Z.-X. (2009). Detecting automotive exhaust gas based on fuzzy inference system. 6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009 3 : 267-270. ScholarBank@NUS Repository. https://doi.org/10.1109/FSKD.2009.137
dc.identifier.isbn9780769537351
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69886
dc.description.abstractThis paper proposes a method of detecting automotive exhaust gas based on fuzzy logic inference after analyzing the principle of the infrared automobile exhaust gas analyzer and the influence of the environmental temperature on analyzer. This paper analyses the measurement error caused by environmental temperature, and then makes a nonlinear error correction of temperature for the infrared sensor using fuzzy inference. The results of simulation have clearly demonstrated that the proposed fuzzy compensation scheme is better than the non-fuzzy method. © 2009 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/FSKD.2009.137
dc.sourceScopus
dc.subjectAutomotive exhaust gas
dc.subjectFuzzy inference
dc.subjectInfrared analyzer
dc.subjectTemperature compensation
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/FSKD.2009.137
dc.description.sourcetitle6th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2009
dc.description.volume3
dc.description.page267-270
dc.identifier.isiut000274887100036
Appears in Collections:Staff Publications

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