Implementation of the RN Method on FPGA using Xilinx System Generator for Nonlinear System Regression

In this paper, we propose a new approach aiming to ameliorate the performances of the regularization networks (RN) method and speed up its computation time. A considerable rapidity in totaling calculation time and high performance were accomplished through conveying difficult calculation charges to FPGA. Using Xilinx System Generator, a successful HW/SW Co-Design was constructed to accelerate the Gramian matrix computation. Experimental results involving two real data sets of Wiener-Hammerstein benchmark with process noise prove the efficiency of the approach. The implementation results demonstrate the efficiency of the heterogeneous architecture, presenting a speed-up factor of 40-50 orders of time, comparing to the CPU simulation.

@article{SAYEHI2017,
  title     = {Implementation of the RN Method on FPGA using Xilinx System Generator for Nonlinear System Regression},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {6},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Intissar SAYEHI and Okba TOUALI and T. Saidani and B. Bouallegue and Mohsen MACHHOUT},
  doi       = {10.14569/IJACSA.2017.080619},
  url       = {https://doi.org/10.14569/IJACSA.2017.080619}
}