Training Feedforward Neural Networks to Predict the Size of the Population by Using a New Hybrid Method Hestenes-Stiefel (HS) and Dai-Yuan (DY)

Hisham MOHAMMED; Khalil K. ABBO; Aydin KHUDHUR

Makale detayları

Training Feedforward Neural Networks to Predict the Size of the Population by Using a New Hybrid Method Hestenes-Stiefel (HS) and Dai-Yuan (DY)

Yazarlar:

Hisham MOHAMMED | Khalil K. ABBO | Aydin KHUDHUR

Yayıncı:

Mahmut DİRİK

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Journal of Soft Computing and Artificial Intelligence 2021 Vol.2 Issue 2, pp.69-78

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000497524

Sıklık:

İki Yıllık

pISSN:

2717-8226

Özet:

 

We proposed a new conjugate gradient type hybrid approach in this study, which is based on merging Hestenes-Stiefel and Dai-Yuan algorithms using the spectral direction conjugate algorithm, we showed their absolute convergence. Under some assumptions and they satisfied the gradient property. The numerical results demonstrate the efficacy of the developed feedforward neural network training approach. To estimate the size of the population using the Thomas Malthus population model, and Our numerical results were very close to the model of the Tomas Malthose Model, we can use the method to predict other problems through the use of ann.

Makale detayları

Training Feedforward Neural Networks to Predict the Size of the Population by Using a New Hybrid Method Hestenes-Stiefel (HS) and Dai-Yuan (DY)

Yazarlar: Hisham MOHAMMED | Khalil K. ABBO | Aydin KHUDHUR

Yayıncı: Mahmut DİRİK

Ana Başlıklar: Özel bilgisayar yöntemleri

Dergi: Journal of Soft Computing and Artificial Intelligence 2021 Vol.2 Issue 2, pp.69-78

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000497524

Sıklık: İki Yıllık

pISSN: 2717-8226

Sayı

Yazarlar:

Hisham MOHAMMED | Khalil K. ABBO | Aydin KHUDHUR

Yayıncı:

Mahmut DİRİK

Ana Başlıklar:

Özel bilgisayar yöntemleri

Dergi:

Journal of Soft Computing and Artificial Intelligence 2021 Vol.2 Issue 2, pp.69-78

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000497524

Sıklık:

İki Yıllık

pISSN:

2717-8226