Self-organizing feature-map-based fuzzy system

Mu Chun Su, Chee Yuen Tew

Research output: Contribution to conferencePaperpeer-review

4 Scopus citations

Abstract

This paper presents a neuro-fuzzy system by using the Kohonen's self-organizing feature map algorithm, not only for its vector quantitization 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 parameters setting and fine-tuning the parameters of the system.

Original languageEnglish
Pages20-25
Number of pages6
StatePublished - 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: 24 Jul 200027 Jul 2000

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy
Period24/07/0027/07/00

Fingerprint

Dive into the research topics of 'Self-organizing feature-map-based fuzzy system'. Together they form a unique fingerprint.

Cite this