Training multilayered perceptrons for pattern recognition: a comparative study of four training algorithms


Pham D., SAĞIROĞLU Ş.

INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, cilt.41, sa.3, ss.419-430, 2001 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 41 Konu: 3
  • Basım Tarihi: 2001
  • Doi Numarası: 10.1016/s0890-6955(00)00073-0
  • Dergi Adı: INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE
  • Sayfa Sayıları: ss.419-430

Özet

This paper presents an overview of four algorithms used for training multilayered perceptron (MLP) neural networks and the results of applying those algorithms to teach different MLPs to recognise control chart patterns and classify wood veneer defects. The algorithms studied are Backpropagation (BP), Quick-prop (QP), Delta-Bar-Delta (DBD) and Extended-Delta-Bar-Delta (EDBD). The results show that, overall, BP was the best algorithm for the two applications tested. (C) 2001 Elsevier Science Ltd. All rights reserved.