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First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization

Author

Listed:
  • Bingjie Zhang

    (School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China)

  • Jian Wang

    (College of Science, China University of Petroleum (East China), Qingdao 266580, China)

  • Xiaoling Gong

    (College of Control Science and Engineering, China University of Petroleum (East China), Qingdao 266580, China)

  • Zhanglei Shi

    (College of Science, China University of Petroleum (East China), Qingdao 266580, China)

  • Chao Zhang

    (School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China)

  • Kai Zhang

    (School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
    School of Science, Qingdao University of Technology, Qingdao 266580, China)

  • El-Sayed M. El-Alfy

    (Fellow SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Interdisciplinary Research Center of Intelligent Secure Systems, Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Sergey V. Ablameyko

    (Faculty of Applied Mathematics and Computer Science, Belarusian State University, 220030 Minsk, Belarus)

Abstract

Nonstationary fuzzy inference systems (NFIS) are able to tackle uncertainties and avoid the difficulty of type-reduction operation. Combining NFIS and neural network, a first-order sparse TSK nonstationary fuzzy neural network (SNFNN-1) is proposed in this paper to improve the interpretability/translatability of neural networks and the self-learning ability of fuzzy rules/sets. The whole architecture of SNFNN-1 can be considered as an integrated model of multiple sub-networks with a variation in center, variation in width or variation in noise. Thus, it is able to model both “intraexpert” and “interexpert” variability. There are two techniques adopted in this network: the Mean Shift-based fuzzy partition and the Group Lasso-based rule selection, which can adaptively generate a suitable number of clusters and select important fuzzy rules, respectively. Quantitative experiments on six UCI datasets demonstrate the effectiveness and robustness of the proposed model.

Suggested Citation

  • Bingjie Zhang & Jian Wang & Xiaoling Gong & Zhanglei Shi & Chao Zhang & Kai Zhang & El-Sayed M. El-Alfy & Sergey V. Ablameyko, 2023. "First-Order Sparse TSK Nonstationary Fuzzy Neural Network Based on the Mean Shift Algorithm and the Group Lasso Regularization," Mathematics, MDPI, vol. 12(1), pages 1-14, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2023:i:1:p:120-:d:1310246
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    References listed on IDEAS

    as
    1. Lunhaojie Liu & Juntao Fei & Xianghua Yang, 2023. "Adaptive Interval Type-2 Fuzzy Neural Network Sliding Mode Control of Nonlinear Systems Using Improved Extended State Observer," Mathematics, MDPI, vol. 11(3), pages 1-20, January.
    2. M. Syed Ali & Gani Stamov & Ivanka Stamova & Tarek F. Ibrahim & Arafa A. Dawood & Fathea M. Osman Birkea, 2023. "Global Asymptotic Stability and Synchronization of Fractional-Order Reaction–Diffusion Fuzzy BAM Neural Networks with Distributed Delays via Hybrid Feedback Controllers," Mathematics, MDPI, vol. 11(20), pages 1-24, October.
    3. Nikolina Ljepava & Aleksandar Jovanović & Aleksandar Aleksić, 2023. "Industrial Application of the ANFIS Algorithm—Customer Satisfaction Assessment in the Dairy Industry," Mathematics, MDPI, vol. 11(19), pages 1-22, October.
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