Neural identification of dynamic systems on FPGA with improved PSO learning |
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Authors: | Mehmet Ali Cavuslu Cihan Karakuzu Fuat Karakaya |
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Affiliation: | 1. Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy;2. LORIA (UMR 7503 CNRS), Université de Lorraine, INRIA Nancy-Grand Est, France;3. École Polytechnique de Montréal, Département de Génie Électrique, Canada |
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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. |
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