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) |
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| Institution: | 1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China;2. School of Computer and Communication Engineering, Tianjin University of Technology, Tianjin 300384, China;3. Department of Electrical Engineering, The University of Suwon, San 2-2 Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, South Korea;4. Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada;5. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia;6. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Faculty of Engineering and Information Technology, University of Technology, Sydney, 15 Broadway, Ultimo, NSW 2007, Australia;2. Information and Communication Technologies Centre, Commonwealth Scientific and Industrial Research Organisation, Sydney, PO Box 76, Epping, NSW 1710, Australia;1. Department of Mathematics and Physics, Fujian University of Technology, Fuzhou, Fujian 350118, China;2. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong;1. College of Automation, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Science, Tianjin University of Technology and Education, Tianjin 300222, China;3. Petroleum Engineering College, Yangtze University, Jingzhou 420400, China;4. College of Mechatronics and Control Engineering, Hubei Normal University, Huangshi 435002, China;1. Department of Computer Engineering, Adana Science and Technology University, Adana, Turkey;2. Department of Biomedical Engineering, University of Cukurova, Adana, Turkey;1. Kanazawa University, Japan;2. Miyazaki University, Japan;3. University of Toyama, Japan |
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| 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. |
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| Keywords: | Hybrid radial basis function neural networks (HRBFNNs) Fuzzy clustering method (FCM) Polynomial fuzzy neurons (PFNs) Principal component analysis (PCA) Genetic algorithm (GA) |
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