Artificial olfaction system

Müştak E. Yalçın; Tuba Ayhan; Ramazan Yeniçeri

doi:10.1007/978-3-030-17840-6_3

Artificial olfaction system

Müştak E. Yalçın^*, Tuba Ayhan, Ramazan Yeniçeri

^*Corresponding author for this work

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Performance of an artificial olfaction system depends on the success and speed of its classification according to the chosen problem. In this chapter, the feature extraction part just before the classification part is exploited to reach a better performance for artificial olfaction systems, and cellular nonlinear network-based feature extraction models are presented. For achieving the best performance for different problems on the same network, a reconfigurable cellular neural network is introduced as a feature extractor.

Original language	English
Title of host publication	SpringerBriefs in Applied Sciences and Technology
Publisher	Springer Verlag
Pages	23-50
Number of pages	28
DOIs	https://doi.org/10.1007/978-3-030-17840-6_3
Publication status	Published - 2020

Publication series

Name	SpringerBriefs in Applied Sciences and Technology
ISSN (Print)	2191-530X
ISSN (Electronic)	2191-5318

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Publisher Copyright:
© The Author(s).

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10.1007/978-3-030-17840-6_3

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Artificial olfaction system

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