Odaklanan Nöron

Ilker Cam; F. Boray Tek

doi:10.1109/SIU.2017.7960632

Odaklanan Nöron

Translated title of the contribution: Focusing neuron

Ilker Cam^*, F. Boray Tek

^*Corresponding author for this work

Isik University

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

1 Citation (Scopus)

Abstract

The traditional neural network topology is not flexible to change during the training process. Every neuron and it's independent weights in the network are part of the solution function. The proposed focusing neuron model utilizes inter-dependent weights produced by a focusing function. This neuron can change it's focus position and aperture. This property allows a flexible-dynamic network topology, which can be trained using conventional back-propagation algorithm. Our experiments show that focusing neuron neural networks achieve higher success than fully connected neural networks.

Translated title of the contribution	Focusing neuron
Original language	Turkish
Title of host publication	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509064946
DOIs	https://doi.org/10.1109/SIU.2017.7960632
Publication status	Published - 27 Jun 2017
Externally published	Yes
Event	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Duration: 15 May 2017 → 18 May 2017

Publication series

Name	2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Conference

Conference	25th Signal Processing and Communications Applications Conference, SIU 2017
Country/Territory	Turkey
City	Antalya
Period	15/05/17 → 18/05/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Access to Document

10.1109/SIU.2017.7960632

Cite this

@inproceedings{f8852cbd522047e3a0b090fa4f8f1a34,

title = "Odaklanan N{\"o}ron",

abstract = "The traditional neural network topology is not flexible to change during the training process. Every neuron and it's independent weights in the network are part of the solution function. The proposed focusing neuron model utilizes inter-dependent weights produced by a focusing function. This neuron can change it's focus position and aperture. This property allows a flexible-dynamic network topology, which can be trained using conventional back-propagation algorithm. Our experiments show that focusing neuron neural networks achieve higher success than fully connected neural networks.",

keywords = "artificial neural network, focusing neuron",

author = "Ilker Cam and Tek, {F. Boray}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Signal Processing and Communications Applications Conference, SIU 2017 ; Conference date: 15-05-2017 Through 18-05-2017",

year = "2017",

month = jun,

day = "27",

doi = "10.1109/SIU.2017.7960632",

language = "T{\"u}rk{\c c}e",

series = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2017 25th Signal Processing and Communications Applications Conference, SIU 2017",

address = "United States",

}

Cam, I & Tek, FB 2017, Odaklanan Nöron. in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017., 7960632, 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Institute of Electrical and Electronics Engineers Inc., 25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Turkey, 15/05/17. https://doi.org/10.1109/SIU.2017.7960632

Odaklanan Nöron. / Cam, Ilker; Tek, F. Boray.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7960632 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

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

TY - GEN

T1 - Odaklanan Nöron

AU - Cam, Ilker

AU - Tek, F. Boray

PY - 2017/6/27

Y1 - 2017/6/27

N2 - The traditional neural network topology is not flexible to change during the training process. Every neuron and it's independent weights in the network are part of the solution function. The proposed focusing neuron model utilizes inter-dependent weights produced by a focusing function. This neuron can change it's focus position and aperture. This property allows a flexible-dynamic network topology, which can be trained using conventional back-propagation algorithm. Our experiments show that focusing neuron neural networks achieve higher success than fully connected neural networks.

AB - The traditional neural network topology is not flexible to change during the training process. Every neuron and it's independent weights in the network are part of the solution function. The proposed focusing neuron model utilizes inter-dependent weights produced by a focusing function. This neuron can change it's focus position and aperture. This property allows a flexible-dynamic network topology, which can be trained using conventional back-propagation algorithm. Our experiments show that focusing neuron neural networks achieve higher success than fully connected neural networks.

KW - artificial neural network

KW - focusing neuron

UR - http://www.scopus.com/inward/record.url?scp=85026303259&partnerID=8YFLogxK

U2 - 10.1109/SIU.2017.7960632

DO - 10.1109/SIU.2017.7960632

M3 - Konferans katkısı

AN - SCOPUS:85026303259

T3 - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017

Y2 - 15 May 2017 through 18 May 2017

ER -

Odaklanan Nöron

Abstract

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Bibliographical note

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