fpga实现的自适应神经网络射频功率放大器建模

2011 IEEE 9th International New Circuits and systems conference Pub Date : 2011-06-26 DOI:10.1109/NEWCAS.2011.5981211

M. Bahoura, Chan-Wang Park

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引用次数: 18

摘要

本文提出了一种fpga实现的神经自适应神经网络射频功率行为建模体系结构。采用基于Xilinx System Generator的DSP和Virtex-6 FPGA ML605评估套件，在FPGA上实现了实值时滞神经网络(RVTDNN)和反向传播(BP)学习算法。给出了一个包含6个隐层神经元的网络，在16-QAM调制测试信号和材料资源需求下所获得的性能。

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FPGA-implementation of an adaptive neural network for RF power amplifier modeling

In this paper, we propose an architecture for FPGA-implementation of neural adaptive neural network RF power behavioral modeling. The real-valued time-delay neural network (RVTDNN) and the backpropagation (BP) learning algorithm were implemented on FPGA using Xilinx System Generator for DSP and the Virtex-6 FPGA ML605 Evaluation Kit. Performances obtained with 16-QAM modulated test signal and material resource requirement are presented for a network of six hidden layer neurons.

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2011 IEEE 9th International New Circuits and systems conference

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