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

^*Bu çalışma için yazışmadan sorumlu yazar

Istanbul Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Bölüm › bilirkişi

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	SpringerBriefs in Applied Sciences and Technology
Yayınlayan	Springer Verlag
Sayfalar	23-50
Sayfa sayısı	28
DOI'lar	https://doi.org/10.1007/978-3-030-17840-6_3
Yayın durumu	Yayınlandı - 2020

Yayın serisi

Adı	SpringerBriefs in Applied Sciences and Technology
ISSN (Basılı)	2191-530X
ISSN (Elektronik)	2191-5318

Bibliyografik not

Publisher Copyright:
© The Author(s).

Belgeye Erişim

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

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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title = "Artificial olfaction system",

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.",

author = "Yal{\c c}ın, {M{\"u}{\c s}tak E.} and Tuba Ayhan and Ramazan Yeni{\c c}eri",

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doi = "10.1007/978-3-030-17840-6_3",

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AU - Yalçın, Müştak E.

AU - Ayhan, Tuba

AU - Yeniçeri, Ramazan

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N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85064946652&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-17840-6_3

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

M3 - Chapter

AN - SCOPUS:85064946652

T3 - SpringerBriefs in Applied Sciences and Technology

SP - 23

EP - 50

BT - SpringerBriefs in Applied Sciences and Technology

PB - Springer Verlag

ER -

Artificial olfaction system

Özet

Yayın serisi

Bibliyografik not

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap