Neural identification of dynamic systems on FPGA with improved PSO learning

► 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.