Construction of a neuron-fuzzy classification model based on feature-extraction approach |
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| Authors: | Nai Ren Guo Tzuu-Hseng S Li |
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| Affiliation: | 1. Department of Electrical Engineering, Tung Fang Design University, Kaohsiung County 82941, Taiwan, ROC;2. aiRobots Laboratory, Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan, ROC;1. Department of Chemistry, Capital Normal University, Beijing, 100048, PR China;2. Dongchangfu District Municipal Public Security Bureau, Liaocheng, Shandong, 252000 PR China;1. The University of Adelaide, School of Agriculture, Food and Wine, PMB 1, Glen Osmond, SA 5064, Australia;2. The Australian Wine Research Institute, P.O. Box 197, Glen Osmond, SA 5064, Australia;1. Faculty of Chemistry, University of Belgrade, Studentski trg 12-16, 11000 Belgrade, Republic of Serbia;2. Faculty of Science and Mathematics, University of Ni?, Vi?egradska 33, 18000 Ni?, Republic of Serbia;3. Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, 11000 Belgrade, Republic of Serbia;1. Department of Chemistry, The University of Texas at Austin, Austin, TX 78712, USA;2. Department of Viticulture and Enology, University of California Davis, Davis, CA 95616, USA;1. St-Hyacinthe Research and Development Centre, Agriculture and Agri-Food Canada, 3600 Casavant Ouest, St-Hyacinthe, Qc, J2S 8E3, Canada;2. St-Jean-sur-Richelieu Research and Development Centre, Agriculture and Agri-Food Canada, 430 Boul. Gouin, St-Jean-sur-Richelieu, Qc, J3B 3E6, Canada;3. R&J Oenology, 2050 Rue Dandurand, Suite 309, Montréal, Qc, H2G 1Y9, Canada;4. Centre de recherche en gastronomie, Institut de tourisme et d''hôtellerie du Québec, 3535 Rue Saint Denis, Montréal, Qc, H2X 3P1, Canada;1. Dipartimento di Scienze - Università degli Studi di Roma Tre, Largo G. Murialdo, 1, I-00146, Roma, Italy;2. Dipartimento di Scienze della Terra, Università degli Studi di Firenze, Via G. La Pira, 4, I-50121, Firenze, Italy |
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| Abstract: | In this paper, a Feature-Extraction Neuron-Fuzzy Classification Model (FENFCM) is proposed that enables the extraction of feature variables and provides the classification results. The proposed classification model synergistically integrates a standard fuzzy inference system and a neural network with supervised learning. The FENFCM automatically generates the fuzzy rules from the numerical data and triangular functions that are used as membership functions both in the feature extraction unit and in the inference unit. To adapt the proposed FENFCM, two modificatory algorithms are applied. First, we utilize Evolutionary Programming (EP) to determine the distribution of fuzzy sets for each feature variable of the feature extraction unit. Second, the Weight Revised Algorithm (WRA) is used to regulate the weight grade of the principal output node of the inference unit. Finally, the proposed FENFCM is validated using two benchmark data sets: the Wine database and the Iris database. Computer simulation results demonstrate that the proposed classification model can provide a sufficiently high classification rate in comparison with that of other models proposed in the literature. |
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