Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Subject: search.filters.subject.Artificial Neural NetworksWoS Q: search.filters.wosQuartile.N/A

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  • Conference Object
    Investigation of the Effect of Artificial Neural Network Performance Parameters and Training Dataset on the Probability Estimate Capacity in Structural Reliability Problems
    (Springer international Publishing Ag, 2024) Koroglu, F. B.; Maguire, M.; Akta, E.
    This study highlights two of the important details of the implementation of artificial neural networks to the structural reliability problems by pointing out the effect of training dataset, and the relationship between the performance parameters (coefficient of determination of train, validation, and test sets) of a network and its probability estimation capacity when it is used as a surrogate model in structural reliability problems. Four numerical examples are covered regarding these key aspects including one that is derived from a real-life reinforced concrete structure. Results have shown that the dataset can affect the probability estimation capacity for complex problems. Furthermore, it is also observed that having a neural network with good performance parameters does not mean that the network always has good probability estimation capacity. However, in order to have a network that can be used for probability estimate purposes, its performance parameters must be at a satisfactory level.
  • Conference Object
    Düşük Sükroz Derişimlerinin Görünür Bölge Spektroskopisi ve Yapay Sinir Aǧları ile Kestirimi
    (IEEE, 2017) Mezgil, Bahadir; Erdogan, Duygu; Alduran, Yesim; Yildiz, Umit Hakan; Yildiz, Ahu Arslan; Bastanlar, Yahn
    Low sucrose concentrations in solutions is estimated by means of localized surface plasmon resonance of immobilized gold nanoparticles. The ultraviolet-visible spectra (UV-Vis) of samples with different sucrose concentrations were prepared and used to train artificial neural networks. In our study, MATLAB Neural Networks Toolbox was used and effect of different input sizes and network structures on the estimation accuracy is investigated. It is observed that using complete spectrum instead of peak point results in higher accuracy.