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Prediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networks

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2018
Authors
Antanasijević, Davor
Antanasijević, Jelena
Pocajt, Viktor
article (publishedVersion)
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Abstract
A dataset containing transition temperature values for 243 bent-core liquid crystal (LC) compounds was used to develop quantitative structure property relationship (QSPR) models using only 2D molecular descriptors and general regression neural network (GRNN). Beside a standard analogue GRNN model, another GRNN model with fuzzy digital response was created with the aim to estimate the prediction error for each compound. Two approaches for the selection of most relevant subset of descriptors, namely the partial mutual information (PMI) and self-organizing maps combined with chi square ranking, were also compared. The best results were obtained using analogue GRNN model based on PMI selected subset (R-2 = 0.91), with the mean absolute error (MAE) lower in comparison with previously published corresponding QSPR models. The digital PMI-GRNN model enabled distinction between high and low accurate predictions, i.e. ones with absolute error higher than mean absolute error (MAE) and others with... absolute error lt = MAE, with the accuracy of 81%.

Keywords:
Digital GRNN model / Prediction error estimation / SOM feature selection / QSPR
Source:
Engineering Applications of Artificial Intelligence, 2018, 71, 251-258
Publisher:
  • Pergamon-Elsevier Science Ltd, Oxford
Funding / projects:
  • info:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS// (RS-172007)
  • info:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172013/RS// (RS-172013)

DOI: 10.1016/j.engappai.2018.03.009

ISSN: 0952-1976

WoS: 000436213000020

Scopus: 2-s2.0-85044440493
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URI
http://TechnoRep.tmf.bg.ac.rs/handle/123456789/3873
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  • Radovi istraživača / Researchers’ publications (TMF)
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Tehnološko-metalurški fakultet

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