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Article type: Research Article
Authors: Zhang, Zhao Zhaoa | Pan, Hao Rana | Zhu, Ying Qinb; *
Affiliations: [a] Institute of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an, China | [b] Departmento de Automatico Control, CINVESTAV-IPN, Mexico City, Mexico
Correspondence: [*] Corresponding author. Ying Qin Zhu, Departmento de Automatico Control, CINVESTAV-IPN, Mexico City, D.F.07360, Mexico. E-mail: yzhu@ctrl.cinvestav.mx.
Abstract: Modular neural networks (MNNs) have garnered substantial attention in the field of nonlinear system modeling. However, even though MNNs require fewer hyperparameters due to their hierarchical structure compared to traditional NNs, determining the optimal module arrangement remains challenging. To address these issues, a novel approach named fuzzy modular neural networks (FMNN) is introduced. This method employs conditional fuzzy clustering and incremental radial basis function (RBF) neural networks to automatically construct sub-modules within the MNN framework. The resultant sub-modules are chosen utilizing a distance-based fuzzy integrative strategy, effectively diminishing the necessity for manual intervention. To showcase the superiority of the FMNN approach, a series of experiments are carried out employing three benchmark examples. These experiments encompass a comparison of modeling accuracy against other extensively employed neural network models. The experimental findings illustrate that FMNN surpasses alternative neural network models in terms of model precision.
Keywords: Automated modeling, fuzzy clustering, modular neural network, radial basis function network
DOI: 10.3233/JIFS-232396
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 3605-3621, 2024
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