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Logical radial basis function networks a hybrid intelligent model for function approximation
Affiliation:1. Arab Organisation For Industrialization, Cairo, Egypt;2. Electronics and Communication Department, Faculty of Engineering, Cairo University, Cairo, Egypt;3. Computer and Systems Department, Electronics Research Institute, Egypt;4. Computer Engineering Department, Faculty of Engineering, Cairo University, Cairo, Egypt;1. Institute of Metrology and Computational Science, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China;2. Institute for Information and System Sciences, Xi’an Jiaotong University, Xi’an 710049, Shannxi Province, PR China;1. Department of Mathematics, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China;2. Institute for Information and System Sciences, Xi’an Jiaotong University, Xi’an 710049, Shannxi Province, PR China;1. Department of Computer Science and Numerical Analysis, University of Córdoba, Campus de Rabanales, Albert Einstein Building, 3rd floor, 14074 - Córdoba, Spain;2. Department of Management and Quantitative Methods, ETEA, Escritor Castilla Aguayo 4, 14004 - Córdoba, Spain
Abstract:The aim of this article is to introduce a new approach for fuzzy neural network models which can be used effectively in function approximation problems. The proposed model is introduced as an adaptive two-level fuzzy inference system. The architecture of the model is basically a two-layer network of new types of fuzzy-neurons which act as fuzzy IF–THEN rules. The model can be considered as a logical version of the Radial Basis Function networks (RBF). Genetic Algorithms have been adopted as the learning mechanism of the proposed model. Simulations show both highly nonlinear mapping and reasoning capabilities together with simpler structure and better performance when compared with classical neural networks.
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