Neural identification of dynamic systems on FPGA with improved PSO learning
Küçük Resim Yok
Tarih
2012
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
ELSEVIER SCIENCE BV
Erişim Hakkı
info:eu-repo/semantics/closedAccess
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. (C) 2012 Elsevier B.V. All rights reserved.
Açıklama
Anahtar Kelimeler
Artificial neural networks (ANN), Particle swarm optimization (PSO), FPGA, System identification
Kaynak
APPLIED SOFT COMPUTING
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
12
Sayı
9
