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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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

Author 1: Intissar SAYEHI
Author 2: Okba TOUALI
Author 3: T. Saidani
Author 4: B. Bouallegue
Author 5: Mohsen MACHHOUT

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.080619

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 6, 2017.

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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.

Keywords: Machine learning; Reproducing Kernel Hilbert Spaces (RKHS); regularization networks; FPGA; HW/SW Co-simulation; systolic array architecture; PT326; Wiener-Hammerstein benchmark

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}
}


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