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SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks

Author

Listed:
  • Jing Wang

    (Guangdong polytechnic Normal University)

  • Shubin Lyu

    (Guangdong polytechnic Normal University)

  • C. L. Philip Chen

    (University of Macau
    South China University of Technology)

  • Huimin Zhao

    (Guangdong polytechnic Normal University)

  • Zhengchun Lin

    (Guangdong polytechnic Normal University)

  • Pingsheng Quan

    (Guangdong polytechnic Normal University)

Abstract

Broad learning system (BLS) is a fast and efficient learning model. However, BLS has limited representation capacity in the feature mapping layer. Additionally, BLS lacks local mapping capability. To address these problems, a cascaded neural network framework based on a sparse polynomial-based RBF neural network and an attention-based broad learning system (SPRBF-ABLS) is proposed. We first propose a sparse polynomial weight-based RBF neural network (SPRBF) for feature mapping. Then an attention mechanism for BLS is proposed to enhance the representation capacity of BLS. The proposed model is evaluated on regression, classification, and face recognition datasets. In regression and classification experiments, the nonlinear approximation capability of the proposed model outperforms other BLS models. In face recognition experiments, the proposed model can improve the representation capacity, especially the robustness against noisy images. The experiments demonstrate the effectiveness and robustness of the proposed model.

Suggested Citation

  • Jing Wang & Shubin Lyu & C. L. Philip Chen & Huimin Zhao & Zhengchun Lin & Pingsheng Quan, 2023. "SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1779-1794, April.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:4:d:10.1007_s10845-021-01897-7
    DOI: 10.1007/s10845-021-01897-7
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    References listed on IDEAS

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    1. Yu Mo & Qianhui Wu & Xiu Li & Biqing Huang, 2021. "Remaining useful life estimation via transformer encoder enhanced by a gated convolutional unit," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1997-2006, October.
    2. Dechen Yao & Hengchang Liu & Jianwei Yang & Jiao Zhang, 2021. "Implementation of a novel algorithm of wheelset and axle box concurrent fault identification based on an efficient neural network with the attention mechanism," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 729-743, March.
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