Incremental Granular Model Improvement Using Particle Swarm Optimization

Clicks: 235
ID: 271478
2019
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
Emerging

Ranked #87 of 145 articles by views in Symmetry

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 145 in total.

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
This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.
Reference Key
yeom2019symmetryincremental Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chan-Uk Yeom;Keun-Chang Kwak;Yeom, Chan-Uk;Kwak, Keun-Chang;
Journal Symmetry
Year 2019
DOI
10.3390/sym11030390
URL
https://doi.org/10.3390/sym11030390
Keywords
prediction particle swarm optimization (pso) context-based fuzzy c-means (cfcm) clustering granular model (gm) linear regression (lr) incremental granular model (igm)

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.