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- W3048989783 abstract "This research aims to introduce a novel radial basis functional link net (RBFLN)-based QSPR (quantitative structure-property relationship) model to predict the solubility parameters of the polymers with the structure – (C1H-2-C2R3R4) – and provides its comparison with the multi-layer feed forward network (MLFFN)-based QSPR model, as well as previous genetic programming (GP) and multiple linear regression (MLR)-based QSPR models in the literature. During the implementation of the RBFLN and MLFFN-based QSPR models, the networks which are associated with the minimum weighted average AIC (Akaike’s information criterion) and BIC (Bayesian information criterion) scores are trained by using a hybrid scheme combining the cuckoo search and Levenberg-Marquardt algorithm. Our results show that the RBFLN-based QSPR model outperforms the other ones in terms of the external validation metrics. The study also reveals that it may have a promising potential to study the relationship between various measurement/experimental data or processing elements in a hybrid way of artificial intelligence modelling." @default.
- W3048989783 created "2020-08-21" @default.
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- W3048989783 date "2021-01-19" @default.
- W3048989783 modified "2023-10-01" @default.
- W3048989783 title "QSPR prediction of polymers’ solubility parameters by radial basis functional link net" @default.
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- W3048989783 doi "https://doi.org/10.3233/jcm-200033" @default.
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