Reconciliation of outliers in CO2-alkanolamine-H2O datasets by robust neural network winsorization

Reconciliation of outliers in CO2-alkanolamine-H2O datasets by robust neural network winsorization It is normal to find at least a few measured values in CO2-alkanolamine-H2O datasets that deviate greatly from the majority of published data, as the data come from different sources. These values, termed as data outliers, are the major source of conflict in modeling, simulation and process development studies. Therefore, removal of data outliers is mandatory. However, available statistical techniques are known to lose information at the boundaries of the system and exhibit substantial deviation from holistic data trend. Hence, an adaptive approach combining artificial neural networks and robust winsorization is presented for identification and reconciliation of data outliers in CO2-alkanolamine-H2O system. The proposed approach flexibly transforms to the nonlinear data distribution and predicts corrected values for data outliers (winsorized values), thus maintaining the information at extremes of the system. The results have been graphically analyzed and show good conformance in treated data, with retention of winsorized values. The proposed method improves the shortcomings of previous statistical approaches and can be potentially extended to other nonlinear experimental datasets in chemical process systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Reconciliation of outliers in CO2-alkanolamine-H2O datasets by robust neural network winsorization

Loading next page...
 
/lp/springer_journal/reconciliation-of-outliers-in-co2-alkanolamine-h2o-datasets-by-robust-w4OqIAEpZm
Publisher
Springer London
Copyright
Copyright © 2016 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
D.O.I.
10.1007/s00521-016-2213-z
Publisher site
See Article on Publisher Site

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial