a neural network model for predicting nox at the mělník 1
Clicks: 99
ID: 260124
2012
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
29.4
/100
99 views
15 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #147 of 188 articles by views in the journal of nutrition
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 188 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
This paper presents a non-conventional dynamic neural network that was designed for real time prediction of NOx at the coal powder power plant Mělnik 1, and results on real data are shown and discussed. The paper also presents the signal preprocessing techniques, the input-reconfigurable architecture, and the learning algorithm of the proposed neural network, which was designed to handle the non-stationarity of the burning process as well as individual failures of the measured variables. The advantages of our designed neural network over conventional neural networks are discussed.
| Reference Key |
bukovsk2012actaa
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Ivo Bukovský;Michal Kolovratník |
| Journal | the journal of nutrition |
| Year | 2012 |
| DOI |
DOI not found
|
| 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.