Neural identification of dynamic systems on FPGA with improved PSO learning

Cavuslu M. A., KARAKUZU C., Karakaya F.

Applied Soft Computing Journal, cilt.12, sa.9, ss.2707-2718, 2012 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 9
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.asoc.2012.03.022
  • Dergi Adı: Applied Soft Computing Journal
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2707-2718
  • Anahtar Kelimeler: Artificial neural networks (ANN), FPGA, Particle swarm optimization (PSO), System identification
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

This work introduces hardware implementation of artificial neural networks (ANNs) with learning ability on field programmable gate array (FPGA) for dynamic system identification. The learning phase is accomplished by using the improved particle swarm optimization (PSO). The improved PSO is obtained by modifying the velocity update function. Adding an extra term to the velocity update function reduced the possibility of stucking in a local minimum. The results indicates that ANN, trained using improved PSO algorithm, converges faster and produces more accurate results with a little extra hardware utilization cost. © 2012 Elsevier B.V. All rights reserved.