Neural identification of dynamic systems on FPGA with improved PSO learning

2012-09-01
Cavuslu, Mehmet Ali
KARAKUZU, CİHAN
KARAKAYA, FUAT
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.
APPLIED SOFT COMPUTING

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Citation Formats
M. A. Cavuslu, C. KARAKUZU, and F. KARAKAYA, “Neural identification of dynamic systems on FPGA with improved PSO learning,” APPLIED SOFT COMPUTING, pp. 2707–2718, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67200.