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Fault Tolerant Broad Learning System

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Book cover Neural Information Processing (ICONIP 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1142))

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Abstract

The broad learning system (BLS) approach provides low computational complexity solutions for training flat structure feedforward networks. However, many BLS algorithms deal with the faultless situation only. This paper addresses the fault tolerant ability of BLS networks. We call our approach fault tolerant BLS (FTBLS). First, we develop a fault tolerant objective function for BLS. Based on the developed objective function, we develop a training algorithm to construct a BLS network. The simulation results show that our proposed FTBLS is much better than the classical BLS.

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Acknowledgments

The work was supported by a research grant from City University of Hong Kong (7005063).

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Authors and Affiliations

  1. Department of Electronic Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

    Muideen Adegoke & Chi-Sing Leung

  2. Institute of Technology Management, National Chung Hsing University, Taichung, Taiwan

    John Sum

Authors
  1. Muideen Adegoke
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  2. Chi-Sing Leung
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  3. John Sum
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Corresponding author

Correspondence to Chi-Sing Leung .

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Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Adegoke, M., Leung, CS., Sum, J. (2019). Fault Tolerant Broad Learning System. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_11

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  • DOI: https://doi.org/10.1007/978-3-030-36808-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36807-4

  • Online ISBN: 978-3-030-36808-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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