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Design of fuzzy radial basis function-based polynomial neural networks |
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| Authors: | Seok-Beom RohSung-Kwun Oh Witold Pedrycz |
| Affiliation: | a Department of Electrical Electronic and Information Engineering, Wonkwang University, 344-2, Shinyong-Dong, Iksan, Chon-Buk 570-749, South Korea b Department of Electrical Engineering, The University of Suwon, San 2-2, Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do 445-743, South Korea c Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada AB T6G 2V4 d School of Computing Science, The Nottingham University, Nottingham NG8 1BB, UK e Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland |
| 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. |
| Keywords: | Radial basis function Fuzzy C-means (FCM) clustering Polynomial neural networks Neurofuzzy systems Virtual input variable Machine learning data |
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