Application of artificial neural networks for Zeta potential of copolymer

One of the measure of stability of the colloidal system is Zeta potential. In this study, artificial neural networks based prediction of the zeta potential of the copolymer was investigated. Multilayered perceptron and generalized regression neural networks were developed for zeta potential measurements of the copolymer as a function of pHs. Performance indices were demonstrate that structured generalized regression neural networks can predict the zeta potential of the copolymer quite efficiently than multilayer perceptron neural networks. The results showed that generalized regression neural networks could be useful to predict the zeta potential of the copolymer at different pH values.