Abstract: 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.

Intissar SAYEHI, Okba TOUALI, T. Saidani , B. Bouallegue and Mohsen MACHHOUT, “Implementation of the RN Method on FPGA using Xilinx System Generator for Nonlinear System Regression” International Journal of Advanced Computer Science and Applications(IJACSA), 8(6), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080619

@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},
doi = {10.14569/IJACSA.2017.080619},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080619},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {6},
author = {Intissar SAYEHI and Okba TOUALI and T. Saidani and B. Bouallegue and Mohsen MACHHOUT}
}