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

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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.

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Code Availability Statement

Matlab2018b, https://www.mathworks.com/.

Data availability

Asuncion A, UCI machine learning repository, http://archive.ics.uci.edu/ml/index.php.

Belhumeur P, Hespanha J, Kriegman D, Eigenfaces vs. fisherfaces:Recognition using class specific linear projectin, yalefaces, http://cvc.cs.yale.edu/cvc/projects/yalefaces/yalefaces.html.

Samaria F, Harter A, arameterisation of a stochastic model for humanface identification, https://cam-orl.co.uk/facedatabase.html.

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Acknowledgements

This work was supported in the Research Platforms and Program of the Education Department of Guangdong Province in China under Grant 2017KTSCX113, and was partly supported by the Key Laboratory of the Education Department of Guangdong Province in China under Grant 2019KSYS009.

Funding

This work was supported in the Research Platforms and Program of the Education Department of Guangdong Province in China (Grant numbers [2017KTSCX113] and [2019KSYS009]).

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Authors and Affiliations

  1. Faculty of Computer Science, Guangdong polytechnic Normal University, Guangzhou, China

    Jing Wang, Shubin Lyu, Huimin Zhao, Zhengchun Lin & Pingsheng Quan

  2. Faculty of Science and Technology, University of Macau, Macau, 99999, China

    C. L. Philip Chen

  3. South China University of Technology, Guangzhou, China

    C. L. Philip Chen

Authors
  1. Jing Wang
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  2. Shubin Lyu
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  3. C. L. Philip Chen
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  4. Huimin Zhao
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  5. Zhengchun Lin
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  6. Pingsheng Quan
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Correspondence to C. L. Philip Chen or Huimin Zhao.

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Wang, J., Lyu, S., Chen, C.L.P. et al. SPRBF-ABLS: a novel attention-based broad learning systems with sparse polynomial-based radial basis function neural networks. J Intell Manuf 34, 1779–1794 (2023). https://doi.org/10.1007/s10845-021-01897-7

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  • DOI: https://doi.org/10.1007/s10845-021-01897-7

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