Neural identification of dynamic systems on FPGA with improved PSO learning

Authors:
Mehmet Ali Cavuslu;Cihan Karakuzu;Fuat Karakaya
Affiliations:
Koc Information and Defense Technologies Inc., METU Technopolis, ODTí-Teknokent, 06800 Ankara, Turkey;Bilecik Seyh Edebali University, Engineering faculty, Department of Computer Engineering, Gülümbe Campus, 11210, Bilecik, Turkey;Nigde University, Engineering Faculty, Department of Electrical and Electronics Engineering, Nigde, Turkey
Venue:
Applied Soft Computing
Year:
2012

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Abstract

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.