A Proposed Annex Architecture for Enhancing Extrapolation Performance of MLP Neural Networks

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Atıf İçin Kopyala

Yılmaz S., Ekinci Ö.

EngOpt2014 - 4th International Conference on Engineering Optimization, Lisbon, Portekiz, 8 - 11 Eylül 2014, ss.149-150

• Yayın Türü: Bildiri / Özet Bildiri
• Basıldığı Şehir: Lisbon
• Basıldığı Ülke: Portekiz
• Sayfa Sayıları: ss.149-150
• Kocaeli Üniversitesi Adresli: Evet

Özet

Limited extrapolation capability of artificial neural networks (ANNs) is noticed and paid attention in several studies. An ANNs based extrapolation model, which includes associated methodologies in a systematic order, is proposed in order to overcome such limitations. Among these methodologies are recursive training, adding new hidden layer units, scaling the input variable in order to bring the inputs into a suitable range and selecting the good performance network architecture after trial and error. Estimation of the peaks flows is an important problem for the design, management and safety of any hydraulic structures. In addition to this problem, the hydrological processes are nonlinear, time varying and spatially distributed. In this study, an annual maximum flows estimation model (AMFE) is developed by means of ANNs, which is trained under the light of these mentioned prescriptions.  In this way, an acceptable extrapolation pattern is achieved for annual maximum flows data of the Cine-Kayirli gauging station in B. Menderes basin,  Turkey. The results are compared with the known statistical maximum flows estimation methods.