Neural identification of dynamic systems on FPGA with improved PSO learning

Date

2012-09-01

Author

Cavuslu, Mehmet Ali
KARAKUZU, CİHAN
KARAKAYA, FUAT

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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.

Subject Keywords

Artificial neural networks (ANN), Particle swarm optimization (PSO), FPGA, System identification

URI

https://hdl.handle.net/11511/67200

Journal

APPLIED SOFT COMPUTING

DOI

https://doi.org/10.1016/j.asoc.2012.03.022

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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.