FPGA implementation of a NARX network for modeling nonlinear systems

Abstract

This paper presents the FPGA implementation of a NARX neural network for the modeling nonlinear systems. The complete neural architecture was implemented with Verilog language in Xilinx ISE Tool with the Virtex-6 FPGA ML605 Evaluation Kit. All operations, such as data processing, weight connections, multipliers, adders and activation function were performed using floating point format, because allows high precision in operations with high complexity. Some resources of Xilinx were used such as multipliers and CORE blocks, and the hyperbolic tangent of the activation is realized based on Taylor series. To validate the implementation results, the NARX network was used to model the inverse characteristics of a power amplifier. The results obtained in the simulation and the FPGA implementation shown a high correspondence.