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Stochastic configuration broad learning system and its approximation capability analysis

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Abstract

In this paper, a kind of stochastic configuration broad learning system (SCBLS) is proposed for data modeling. The proposed SCBLS is established in the form of a flat network and its architecture is determined by a constructive learning approach. The input parameters of feature nodes and enhancement nodes of SCBLS are randomly assigned in the light of a supervisory mechanism. Inequality constraints are used to randomly assign the hidden parameters and adaptively select the scopes of random parameters. The output parameters of SCBLS are determined either by a constructive manner or by solving a global least squares problem. It is proved that the proposed SCBLS possesses universal approximation properties. The performances of the proposed SCBLS are evaluated by function approximation, benchmark datasets and time series prediction. Numerical examples show that SCBLS can achieve satisfactory approximation accuracy.

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Availability of data and material

All data used during this study are available from the corresponding author on reasonable request.

Code availability

The code used during the current study are available from the corresponding author on reasonable request

Notes

  1. KEEL: http://www.keel.es/

  2. TSDL: https:/ /datamarket.com /data /list /?q = provider:tsdl.

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Acknowledgements

This work is supported by the National Natural Science Foundation (NNSF) of China under Grant (61773088, 12071056), the Fundamental Research Funds for the Central Universities (DUT20JC30) and the National Key R&D Program of China (2018AAA0100300).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, China

    Wei Zhou, Degang Wang & Hongxing Li

  2. School of Control Science and Engineering, Dalian University of Technology, Dalian, China

    Wei Zhou, Degang Wang & Hongxing Li

  3. School of Applied Mathematics, Beijing Normal University, Zhuhai, China

    Wei Zhou & Menghong Bao

Authors
  1. Wei Zhou
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  2. Degang Wang
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  3. Hongxing Li
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  4. Menghong Bao
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Correspondence to Degang Wang.

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Zhou, W., Wang, D., Li, H. et al. Stochastic configuration broad learning system and its approximation capability analysis. Int. J. Mach. Learn. & Cyber. 13, 797–810 (2022). https://doi.org/10.1007/s13042-021-01341-5

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  • DOI: https://doi.org/10.1007/s13042-021-01341-5

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