Modeling of CO distribution in Istanbul using Artificial Neural Networks

Sahin U., Ucan O., Soyhan B., Bayat C.

FRESENIUS ENVIRONMENTAL BULLETIN, cilt.13, sa.9, ss.839-845, 2004 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 9
  • Basım Tarihi: 2004
  • Dergi Adı: FRESENIUS ENVIRONMENTAL BULLETIN
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.839-845
  • İstanbul Üniversitesi Adresli: Hayır

Özet

Artificial Neural Network (ANN) is one of the popular methods in optimization of complex engineering problems compared to the classical statistical methods. ANN approximates non-linear input-output variables and finds an optimum correlation between these variables. Thus the structure of the overall system is simplified. ANN function approximation is achieved by identifying the input-output pattern pairs, using the following steps: (I) Selection of the neural structure (namely the number of layers and that of neurons), (II) Training of ANN using Back-Propagation (BP) algorithms. ANN coefficients can be trained as any system performance characteristics by monitoring test data. (III) Validation of the network to verify generalization capability.