Abstract
In this study, we introduce a new design methodology of fuzzy radial basis function-based polynomial neural networks. In many cases, these models do not come with capabilities to deal with granular information. With this regard, fuzzy sets offer several interesting and useful opportunities. This study presents the development of fuzzy radial basis function-based neural networks augmented with virtual input variables. The performance of the proposed category of models is quantified through a series of experiments, in which we use two machine learning data sets and two publicly available software development effort data.
► We introduce a new fuzzy radial basis function-based polynomial neural network. ► To deal with granular information, a sort of Fuzzy Neural Networks is presented. ► We define the virtual input variables through Polynomial Neural Networks.