Hardware implemented adaptive neuro fuzzy system

S. Brassai, Szabolcs Hajdú, T. Tamas, L. Bakó
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引用次数: 3

Abstract

In the paper the implementation on reconfigurable hardware of a Sugeno type neuro adaptive fuzzy inference system is proposed to be presented. The pipeline and parallel pipeline architecture play an important role in modelling the algorithm for the FPGA based implementation. In order to design the pipeline-parallel model of the controller two different methods were used: high level synthesis tool respectively System Generator. Some of the inference systems sub-modules were implemented in VHDL. The proposed hardware model's processing speed is very high, allows the controller to be used in real-time applications.
硬件实现自适应神经模糊系统
本文提出了一种Sugeno型神经自适应模糊推理系统的可重构硬件实现。流水线和并行流水线结构对FPGA实现的算法建模起着重要的作用。为了设计控制器的流水线-并行模型,分别采用了两种不同的方法:高级综合工具System Generator。部分推理系统子模块用VHDL语言实现。所提出的硬件模型的处理速度非常快,可以用于实时应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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