Haar Wavelet Neural Network Model Haar Dalgacik Sinir Aǧi Modeli

Pala T., Yücedaǧ I., KAHRAMAN H. T., Güvenç U., SÖNMEZ Y.

2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018, Malatya, Türkiye, 28 - 30 Eylül 2018 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/idap.2018.8620855
  • Basıldığı Şehir: Malatya
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Convolutional neural network (CNN), Deep learning, Haar Wavelet Transfrom
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

© 2018 IEEE.Convolutional neural networks, one of the most important methods of deep learning which is a popular and modern research topic. Nowadays, thismethod has been applied many problems in a short time and obtained successful results for science and the industry. The multi-layer structure adopted in the design of the convolutional neural network increases the network depth and thus leads to significant problems. In this study, Haar Wavelet Transform-based neural network structure is proposed. Proposed model reduces complexity and number of layers in the network structure. Performance ratios of the proposed model and the conventional model were tested on benchmark MNIST dataset. As a result, when the proposed Haar Wavelet Neural Network model and the convolutional neural network model are compared the accuracy is increased and running time is 6.5 times faster.