Multi-layer architecture for adaptive fuzzy inference system with a large number of input features |
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| Institution: | 1. College of Mechanical & Electrical Engineering, Shaanxi University of Science & Technology, Xi’an 710021, China;2. School of Information & Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China;3. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark 07102, USA;4. School of Electro-Mechanical Engineering, Xidian University, Xi’an 710071, China;5. Laboratory CEDRIC, Conservatoire National des Arts et Métiers, 192 rue Saint Martin, 75141 Paris Cedex 03, France;6. Industrial Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia;7. School of Electrical and Information Engineering, Jinan University (Zhuhai Campus), Zhuhai 519070, China;8. Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau;1. Laboratoire LGI2P, école des mines d’Alès, Site de Nîmes, Parc Scientifique Georges Besse, 30 035 Nîmes cedex 1, France;2. M2M-NDT, 1 rue de Terre Neuve, Miniparc du Verger, bâtiment H, Les Ulis, 91 940, France;3. IRIT, équipe Macao, université Paul Sabatier, 118 Route de Narbonne, 31 062 Toulouse, cedex 9, France |
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| Abstract: | The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method. |
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| Keywords: | Adaptive fuzzy neural network 365-day concrete Takagi-sugeno Multi-layer ANFIS Limitation |
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