@book{
 title = {Evolving fuzzy neural networks applied to odor recognition},
 type = {book},
 year = {2004},
 source = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
 volume = {3316},
 id = {2b7fdb9d-4a74-32c5-9464-efeac3df1dae},
 created = {2019-02-14T18:02:00.426Z},
 file_attached = {false},
 profile_id = {74e7d4ea-3dac-3118-aab9-511a5b337e8f},
 last_modified = {2019-02-14T18:02:00.426Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper presents the use of Evolving Fuzzy Neural Networks as pattern recognition system for odor recognition in an artificial nose. In the classification of gases derived from the petroliferous industry, the method presented achieves better results (mean classification error of 0.88%) than those obtained by Multi-Layer Perceptron (13.88%) and Time Delay Neural Networks (10.54%). © Springer-Verlag Berlin Heidelberg 2004.},
 bibtype = {book},
 author = {Zanchettin, C. and Ludermir, T.B.}
}

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