Show simple item record

dc.creatorAntanasijević, Davor
dc.creatorAntanasijević, Jelena
dc.creatorPocajt, Viktor
dc.date.accessioned2021-03-10T13:42:38Z
dc.date.available2021-03-10T13:42:38Z
dc.date.issued2018
dc.identifier.issn0952-1976
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3873
dc.description.abstractA 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%.en
dc.publisherPergamon-Elsevier Science Ltd, Oxford
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172013/RS//
dc.rightsrestrictedAccess
dc.sourceEngineering Applications of Artificial Intelligence
dc.subjectDigital GRNN modelen
dc.subjectPrediction error estimationen
dc.subjectSOM feature selectionen
dc.subjectQSPRen
dc.titlePrediction of the transition temperature of bent-core liquid crystals using fuzzy "digital thermometer" model based on artificial neural networksen
dc.typearticle
dc.rights.licenseARR
dc.citation.epage258
dc.citation.other71: 251-258
dc.citation.rankM21
dc.citation.spage251
dc.citation.volume71
dc.identifier.doi10.1016/j.engappai.2018.03.009
dc.identifier.rcubconv_5608
dc.identifier.scopus2-s2.0-85044440493
dc.identifier.wos000436213000020
dc.type.versionpublishedVersion


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record