Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

Okan ERKAYMAZ; Mahmut ÖZER; Nejat YUMUŞAK

Makale detayları

Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

Yazarlar:

Okan ERKAYMAZ | Mahmut ÖZER | Nejat YUMUŞAK

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 3, pp.708-718

Belge Tipi:

Makale

Yayın Tarihi:

5/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000743007

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

Since feed-forward artificial neural networks (FFANNs) are the most widely  used models to solve real-life problems, many studies have focused on  improving their learning performances by changing the network architecture  and learning algorithms. On the other hand, recently, small-world network  topology has been shown to meet the characteristics of real-life problems.  Therefore, in this study, instead of focusing on the performance of the  conventional FFANNs, we investigated how real-life problems can be solved by  a FFANN with small-world topology. Therefore, we considered 2 real-life  problems: estimating the thermal performance of solar air collectors and  predicting the modulus of rupture values of oriented strand boards. We used  the FFANN with small-world topology to solve both problems and compared the  results with those of a conventional FFANN with zero rewiring. In addition,  we investigated whether there was statistically significant difference  between the regular FFANN and small-world FFANN model. Our results show that  there exists an optimal rewiring number within the small-world topology that  warrants the best performance for both problems.

 

Since feed-forward artificial neural networks (FFANNs) are the most widely  used models to solve real-life problems, many studies have focused on  improving their learning performances by changing the network architecture  and learning algorithms. On the other hand, recently, small-world network  topology has been shown to meet the characteristics of real-life problems.  Therefore, in this study, instead of focusing on the performance of the  conventional FFANNs, we investigated how real-life problems can be solved by  a FFANN with small-world topology. Therefore, we considered 2 real-life  problems: estimating the thermal performance of solar air collectors and  predicting the modulus of rupture values of oriented strand boards. We used  the FFANN with small-world topology to solve both problems and compared the  results with those of a conventional FFANN with zero rewiring. In addition,  we investigated whether there was statistically significant difference  between the regular FFANN and small-world FFANN model. Our results show that  there exists an optimal rewiring number within the small-world topology that  warrants the best performance for both problems.

Makale detayları

Impact of small-world topology on the performance of a feed-forward artificial neural network based on 2 different real-life problems

Yazarlar: Okan ERKAYMAZ | Mahmut ÖZER | Nejat YUMUŞAK

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 3, pp.708-718

Belge Tipi: Makale

Yayın Tarihi: 5/2014

Belge Dili: Türkçe

Document ID (Org No.): 2000743007

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Okan ERKAYMAZ | Mahmut ÖZER | Nejat YUMUŞAK

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2014 Vol.22 Issue 3, pp.708-718

Belge Tipi:

Makale

Yayın Tarihi:

5/2014

Belge Dili:

Türkçe

Document ID (Org No.):

2000743007

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632