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Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion

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Abstract

In this work, a discrete-time noise-tolerant Zhang neural network (DTNTZNN) model is proposed, developed, and investigated for dynamic matrix pseudoinversion. Theoretical analyses show that the proposed DTNTZNN model is inherently tolerant to noises and can simultaneously deal with different types of noise. For comparison, the discrete-time conventional Zhang neural network (DTCZNN) model is also presented and analyzed to solve the same dynamic problem. Numerical examples and results demonstrate the efficacy and superiority of the proposed DTNTZNN model for dynamic matrix pseudoinversion in the presence of various types of noise.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (with numbers 61563017 and 61503152), by the Hunan Natural Science Foundation of China (with numbers 2017JJ3258 and 2017JJ3257), by the Research Foundation of Education Bureau of Hunan Province, China (with numbers 17B215 and 17C1299)).

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Authors and Affiliations

  1. College of Mathematics and Statistics, Jishou University, Jishou, 416000, China

    Qiuhong Xiang

  2. College of Information Science and Engineering, Jishou University, Jishou, 416000, China

    Bolin Liao & Lin Xiao

  3. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Long Lin

  4. Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong

    Shuai Li

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  1. Qiuhong Xiang
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  2. Bolin Liao
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  3. Lin Xiao
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  4. Long Lin
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  5. Shuai Li
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Corresponding author

Correspondence to Bolin Liao.

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All authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by A. Di Nola.

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Xiang, Q., Liao, B., Xiao, L. et al. Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion. Soft Comput 23, 755–766 (2019). https://doi.org/10.1007/s00500-018-3119-8

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  • DOI: https://doi.org/10.1007/s00500-018-3119-8

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