Durağan Zaman Serilerinin Yapay Sinir Ağları ile Tahmininde Girdi Nöronu ve Gizli Nöron Sayısının Belirlenmesi
Bu çalışmada, yapay sinir ağlarının durağan zaman serileri ile geleceği tahminde performansını etkileyen girdi nöronu sayısı ve gizli nöron sayısı parametrelerinin en iyi değerinin belirlenmesi amacı ile bir deney tasarımı yapılmıştır. Ayrıca, Box-Jenkins modelleri ile yapay sinir ağı tekniğinin geleceği tahmindeki başarısı karşılaştırılmış hangi yöntemin daha iyi sonuç verdiği araştırılmıştır.
Anahtar Kelimeler:
Box-Jenkins Modelleri, Girdi Nöronu Sayısı, Gizli Nöron Sayısı Yapay Sinir Ağı
Determining Input and Hidden Neurons Numbers in Artificial Neural Networks for Forecasting Stationary Time Series
In this study, an experimental design has been conducted for determining the optimum values of input and hidden neurons numbers which are the factors affecting the performance of arrificial neural networks used to forecast stationary time series. Furthermore, results of Box-Jenkins models and artificial neural networks are compared and it is also investigated which method gives the better result.
Keywords:
Artificial Neural Networks Number of Input Neurons, Number of Hidden Neurons, Box Jenkins Models,
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- ISSN: 1303-6319
- Başlangıç: 2002
- Yayıncı: TÜİK
Sayıdaki Diğer Makaleler
Sıralı Küme Örneklemesinde Yardımcı Değişken Kullanılarak Yığın Ortalamasının Tahmini