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Sensors 2014, 14(10), 18223-18243; doi:10.3390/s141018223

Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA

Department of Computer Engineering and Automation, Center of Technology, Federal University of Rio Grande do Norte—UFRN, Natal 59078-970, Brazil
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Received: 6 July 2014 / Revised: 16 September 2014 / Accepted: 16 September 2014 / Published: 29 September 2014
(This article belongs to the Section Physical Sensors)
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Abstract

This paper proposes a parallel fixed point radial basis function (RBF) artificial neural network (ANN), implemented in a field programmable gate array (FPGA) trained online with a least mean square (LMS) algorithm. The processing time and occupied area were analyzed for various fixed point formats. The problems of precision of the ANN response for nonlinear classification using the XOR gate and interpolation using the sine function were also analyzed in a hardware implementation. The entire project was developed using the System Generator platform (Xilinx), with a Virtex-6 xc6vcx240t-1ff1156 as the target FPGA. View Full-Text
Keywords: artificial neural network; ANN; radial basis function; RBF; FPGA; fixed point; Simulink; system generator artificial neural network; ANN; radial basis function; RBF; FPGA; fixed point; Simulink; system generator
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

de Souza, A.C.D.; Fernandes, M.A.C. Parallel Fixed Point Implementation of a Radial Basis Function Network in an FPGA. Sensors 2014, 14, 18223-18243.

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