Implementation of adaptive fuzzy neuro generalized learning vector quantization (AFNGLVQ) on field programmable gate array (FPGA) for real world application

Irfan Afif, Yulistiyan Wardhana, W. Jatmiko
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引用次数: 4

Abstract

Microprocessor is needed to be implemented in micro-scale and smaller device cause of its limitation in its resources. One of the microprocessor function is to process a classification and detection method with its inputs. This research is proposed microprocessor design of one of classification algorithm, AFNGLVQ, on FPGA. Compared to its alternative algorithm that has been also implemented in FPGA, FNGLVQ, AFNGLQ gives slightly better result that indicate the algorithm has been successfully implemented in FPGA. The comparison with AFNGLVQ's higher level language implementation also shows that the FPGA design is worth enough to be implemented in micro-scale devices.
在现场可编程门阵列(FPGA)上实现自适应模糊神经广义学习向量量化(AFNGLVQ)
由于微处理器的资源有限,需要在微尺度和更小的器件上实现。微处理器的功能之一是通过其输入处理分类和检测方法。本研究提出了一种基于FPGA的分类算法AFNGLVQ的微处理器设计。与已在FPGA上实现的替代算法FNGLVQ相比,AFNGLQ给出的结果略好,表明该算法已在FPGA上成功实现。通过与AFNGLVQ的高级语言实现的比较,也表明了FPGA设计在微尺度器件上的实现价值。
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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