a new svm-based modeling method of cabin path loss prediction
Clicks: 196
ID: 170470
2013
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.
Reader Engagement
Steady Performance
30.0
/100
196 views
23 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #79 of 304 articles by views in american journal of physiology endocrinology and metabolism
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 304 in total.
Mint this article as an NFT
Not yet mintedCreate 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
Abstract
A new modeling method of cabin path loss prediction based on support vector machine (SVM) is proposed in this paper. The method is trained with the path loss values of measured points inside the cabin and can be used to predict the path loss values of the unmeasured points. The experimental results demonstrate that our modeling method is more accurate than the curve fitting method. This SVM-based path loss prediction method makes the prediction much easier and more accurate, which covers performance traditional methods in the channel propagation modeling.
| Reference Key |
zhao2013internationala
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Xiaonan Zhao;Chunping Hou;Qing Wang |
| Journal | american journal of physiology endocrinology and metabolism |
| Year | 2013 |
| DOI |
10.1155/2013/279070
|
| URL | |
| Keywords |
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.