title: Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs).
abstract: In this study, we propose Hybrid Radial Basis Function Neural Networks (HRBFNNs) realized with the aid of fuzzy clustering method (Fuzzy C-Means, FCM) and polynomial neural networks. Fuzzy clustering used to form information granulation is employed to overcome a possible curse of dimensionality, while the polynomial neural network is utilized to build local models. Furthermore, genetic algorithm (GA) is exploited here to optimize the essential design parameters of the model (including fuzzification coefficient, the number of input polynomial fuzzy neurons (PFNs), and a collection of the specific subset of input PFNs) of the network. To reduce dimensionality of the input space, principal component analysis (PCA) is considered as a sound preprocessing vehicle. The performance of the HRBFNNs is quantified through a series of experiments, in which we use several modeling benchmarks of different levels of complexity (different number of input variables and the number of available data). A comparative analysis reveals that the proposed HRBFNNs exhibit higher accuracy in comparison to the accuracy produced by some models reported previously in the literature.
authors.affiliation: State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China; School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Huang
authors.affiliation: Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea.
authors.forename: Sung-Kwun
authors.initials: SK
authors.lastname: Oh
authors.affiliation: Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland.
authors.forename: Witold
authors.initials: W
authors.lastname: Pedrycz
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D017143:Fuzzy Logic; D015233:Models, Statistical; D016571:Neural Networks, Computer; D025341:Principal Component Analysis
keywords: Fuzzy clustering method (FCM); Genetic algorithm (GA); Hybrid radial basis function neural networks (HRBFNNs); Polynomial fuzzy neurons (PFNs); Principal component analysis (PCA)