FPGA implementation of neuro-fuzzy system with improved PSO learning

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

Neural Networks, cilt.79, ss.128-140, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 79
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1016/j.neunet.2016.02.004
  • Dergi Adı: Neural Networks
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.128-140
  • Anahtar Kelimeler: FPGA implementation, Metaheuristic learning, Neuro-fuzzy network, System identification, VHDL
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

© 2016 Elsevier Ltd.This paper presents the first hardware implementation of neuro-fuzzy system (NFS) with its metaheuristic learning ability on field programmable gate array (FPGA). Metaheuristic learning of NFS for all of its parameters is accomplished by using the improved particle swarm optimization (iPSO). As a second novelty, a new functional approach, which does not require any memory and multiplier usage, is proposed for the Gaussian membership functions of NFS. NFS and its learning using iPSO are implemented on Xilinx Virtex5 xc5vlx110-3ff1153 and efficiency of the proposed implementation tested on two dynamic system identification problems and licence plate detection problem as a practical application. Results indicate that proposed NFS implementation and membership function approximation is as effective as the other approaches available in the literature but requires less hardware resources.