@inProceedings{
 title = {e-NOSE response classification of sewage odors by neural networks and fuzzy clustering},
 type = {inProceedings},
 year = {2005},
 identifiers = {[object Object]},
 pages = {648-651},
 volume = {3611},
 series = {LECTURE NOTES IN COMPUTER SCIENCE},
 editors = {[object Object]},
 id = {f4afb940-c743-32b6-906f-64f588ca26ae},
 created = {2016-04-28T08:46:02.000Z},
 file_attached = {false},
 profile_id = {dce7c6b2-57cf-350f-b364-3e8ed99bb344},
 group_id = {bfd80d76-e42d-36f1-b5b9-353e1a47eb95},
 last_modified = {2016-04-28T08:46:28.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {ISI:000232222500092},
 source_type = {inproceedings},
 notes = {1st International Conference on Natural Computation (ICNC 2005),<br/>Changsha, PEOPLES R CHINA, AUG 27-29, 2005},
 abstract = {Each stage of the sewage treatment process emits odor causing compounds
and these compounds may vary from one location in a sewage treatment
works to another. In order to determine the boundaries of legal
standards, reliable and efficient odor measurement methods need to be
defined. An electronic NOSE equipped with 12 different polypyrrole
sensors is used for the purpose of characterizing sewage odors. Samples
collected at different locations of a WWTP were classified using a
fuzzy clustering technique and a neural network trained with a
back-propagation algorithm.},
 bibtype = {inProceedings},
 author = {Onkal-Engin, G and Demir, I and Engin, S N},
 booktitle = {ADVANCES IN NATURAL COMPUTATION, PT 2, PROCEEDINGS}
}

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