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Review of Pseudoinverse Learning Algorithm for Multilayer Neural Networks and Applications

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Advances in Neural Networks – ISNN 2018 (ISNN 2018)

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Abstract

In this work, we give an overview of pseudoinverse learning (PIL) algorithm as well as applications. PIL algorithm is a non-gradient descent algorithm for multi-layer perception. The weight matrix of network can be exactly computed by PIL algorithm. So PIL algorithm can effectively avoid the problem of low convergence and local minima. Moreover, PIL does not require user-selected parameters, such as step size and learning rate. This algorithm has achieved good application in the fields of software reliability engineering, astronomical data analysis and so on.

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Acknowledgements

This work is fully supported by the grants from the Joint Re-search Fund in Astronomy (Grant No. U1531242) under cooperative agreement between the National Natural Science Foundation of China (NSFC) and Chinese Academy of Sciences (CAS), Prof. Ping Guo is the author to whom all correspondence should be addressed.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100083, China

    Jue Wang, Ping Guo & Xin Xin

  2. School of Systems Science, Beijing Normal University, Beijing, 100875, China

    Ping Guo

  3. School of Space Information, Space Engineering University, Beijing, 101400, China

    Jue Wang

Authors
  1. Jue Wang
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  2. Ping Guo
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  3. Xin Xin
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Corresponding author

Correspondence to Ping Guo .

Editor information

Editors and Affiliations

  1. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

  2. Sichuan University, Chengdu, China

    Jiancheng Lv

  3. Southeast University, Nanjing, China

    Changyin Sun

  4. United Institute of Informatics Problems, Minsk, Belarus

    Alexander V. Tuzikov

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Wang, J., Guo, P., Xin, X. (2018). Review of Pseudoinverse Learning Algorithm for Multilayer Neural Networks and Applications. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_12

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  • DOI: https://doi.org/10.1007/978-3-319-92537-0_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92536-3

  • Online ISBN: 978-3-319-92537-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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