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

Irfan Nur Afif, Yulistiyan Wardhana, Wisnu Jatmiko

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages65-71
Number of pages7
ISBN (Electronic)9781509003624
DOIs
Publication statusPublished - 19 Feb 2016
EventInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015 - Depok, Indonesia
Duration: 10 Oct 201511 Oct 2015

Publication series

NameICACSIS 2015 - 2015 International Conference on Advanced Computer Science and Information Systems, Proceedings

Conference

ConferenceInternational Conference on Advanced Computer Science and Information Systems, ICACSIS 2015
Country/TerritoryIndonesia
CityDepok
Period10/10/1511/10/15

Keywords

  • AFNGLVQ
  • classification
  • FNGLVQ
  • FPGA

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