Yapay Sinir Ağları Yöntemi ile İkinci Kuşak Akım Taşıyıcının Performans Parametrelerinin Tahmin Edilmesi

Yıl 2019, Cilt: 1 Sayı: 1, 13 - 23, 31.10.2019

Abdullah Yeşil , Ufuk Çelik , Feyzullah Temurtaş

https://doi.org/10.46387/bjesr.632631

Cited By: 1

Öz

Bu çalışmada ikinci kuşak akım taşıyıcı (CCII) için yapay sinir
ağları ile performans parametreleri tahmin edilmiştir. Öncelikle seçilen
CCII’nin CMOS gerçeklemesindeki transistörlerin kanal boyu ve genişliği ile
kutuplama akımı sistematik olarak LTSPICE benzetim programında taratılmıştır.
Ardından CCII’nin CMOS gerçeklemesi göz önünde bulundurularak benzetim programı
aracılığıyla dört adet performans parametresi elde edilmiştir. Performans
parametrelerinin eğitimi ve tahminlemesi için yapay sinir ağları yöntemi
kullanılmıştır. Beyin sinir sisteminin bir simülasyonu olan yapay sinir ağları
(YSA) büyük verilerin işlenmesinde kullanılan algoritmalardan birisidir. Yapay
sinir ağları gerçekleştirilen tahminleme analiz sonuçlarına göre 24300 veride 19440
eğitim ile 4860 adet test verisi için ortalama mutlak yüzde hatası (MAPE) %7.33
olarak bulunmuştur.

Anahtar Kelimeler

Aktif elemanlar, Yapay Sinir Ağları

Destekleyen Kurum

Bandırma Onyedi Eylül Üniversitesi

Proje Numarası

BAP-18-DF-1003-004

Teşekkür

Bu çalışma Bandırma Onyedi Eylül Üniversitesi Bilimsel Araştırma Projeleri Koordinasyon Birimi tarafından desteklenmiştir. Proje Numarası: BAP-18-DF-1003-004.

Performance Parameters Estimation of Second Generation Current Conveyor with Artificial Neural Networks

Öz

In this study, performance parameters were
estimated with artificial neural networks for the second generation current
conveyor (CCII). First, the channel length and width of the transistors in the
CMOS implementation of the selected CCII and the biasing current were
systematically scanned in the LTSPICE simulation program. Then, considering the
CMOS implementation of CCII, four performance parameters were obtained through
the simulation program. Artificial neural network method was used for training
and estimation of performance parameters. Artificial neural networks (ANNs),
which is a simulation of the brain nervous system, are one of the algorithms
used in the processing of big data. According to the estimation analysis
results of artificial neural networks, mean absolute percentage error (MAPE)
for 4860 test and 19440 training in 24300 data was found to be 7.33%.

Anahtar Kelimeler

Active elements, Artificial Neural networks

Proje Numarası

BAP-18-DF-1003-004

Kaynakça

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