개선된 컨텍스트기반 퍼지 클러스터링을 이용한 점진적 입자모델의 설계와 자동적인 지식생성

Clicks: 80
ID: 117950
1970
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Star

Ranked #2 of 2 articles by views in 한국정보기술학회논문지

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
본 논문은 점진적 입자 모델을 설계하기 위해 선형회귀(LR: Linear Regression)와 국소 입자모델(LGM: Local Granular Model)을 결합한다. 여기서, 국소 입자모델은 자동적인 지식생성을 위해 밀도 피크의 빠른 탐색에 근거한 컨텍스트기반 퍼지 클러스터링(Context-based fuzzy clustering)에 의해 설계되어진다
Reference Key
염찬욱1970한국정보기술학회논문지개선된 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors 염찬욱;곽근창;
Journal 한국정보기술학회논문지
Year 1970
DOI
DOI not found
URL
https://doi.org/10.14801/jkiit.2020.18.2.17
Keywords
개선된 컨텍스트기반 퍼지 클러스터링을 이용한 점진적 입자모델의 설계와 자동적인 지식생성 automatic knowledge generation and design of incremental granular model using improved context-based fuzzy clustering automatic knowledge generation;incremental granular model;local granular model;information granules; 염찬욱 곽근창 한국정보기술학회논문지(the journal of korean institute of information technology) 한국정보기술학회논문지 제18권 제2호(jkiit vol.18 no.2) 한국정보기술학회(korean institute of information technology)

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.