A SOM-based fuzzy system and its application in handwritten digit recognition

Mu Chun Su, E. Lai, Chee Yuen Tew

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

8 Scopus citations

Abstract

The paper presents a neuro-fuzzy system by using Kohonen's self-organizing feature map algorithm, not only for its vector quantization feature, but also for its topological property. This property prevents the proposed neuro-fuzzy system from suffering from a drawback like any of the conventional clustering algorithm based fuzzy systems, i.e. the optimal number of clusters or some kind of similarity threshold must be predetermined. Associated with the self-organizing feature map based fuzzy system is a hybrid learning algorithm, which is for initial parameter setting and fine-tuning the parameters of the system. Application of the proposed fuzzy systems in optical handwritten digit recognition is reported. High recognition rates can be achieved.

Original languageEnglish
Title of host publicationProceedings - International Symposium on Multimedia Software Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages253-258
Number of pages6
ISBN (Electronic)0769509339, 9780769509334
DOIs
StatePublished - 2000
EventInternational Symposium on Multimedia Software Engineering, MSE 2000 - Taipei, Taiwan
Duration: 11 Dec 200013 Dec 2000

Publication series

NameProceedings - International Symposium on Multimedia Software Engineering

Conference

ConferenceInternational Symposium on Multimedia Software Engineering, MSE 2000
Country/TerritoryTaiwan
CityTaipei
Period11/12/0013/12/00

Keywords

  • fuzzy systems
  • neural networks
  • optical character recognition
  • self-organizing feature map

Fingerprint

Dive into the research topics of 'A SOM-based fuzzy system and its application in handwritten digit recognition'. Together they form a unique fingerprint.

Cite this