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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 – (C ${}^{1}$ H ${}_{2}--$ C ${}^{2}$ R ${}^{3}$ R ${}^{4}$ ) – 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.

Keywords

Polymer solubility neural networks radial basis functional link net QSPR cuckoo searchArticle Highlights:•The RBFLN based QSPR model accurately predicts the solubility parameters.•The RBFLN is able to provide a much simpler solution than the MLFFN.•The hybridization of CS and LM algorithms is an effective training strategy.

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QSPR prediction of polymers’ solubility parameters by radial basis functional link net

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