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Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding

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Neural Information Processing (ICONIP 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1961))

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Abstract

Square root finding plays an important role in many scientific and engineering fields, such as optimization, signal processing and state estimation, but existing research mainly focuses on solving the time-invariant matrix square root problem. So far, few researchers have studied the time-varying tensor square root (TVTSR) problem. In this study, a novel anti-interference zeroing neural network (AIZNN) model is proposed to solve TVTSR problem online. With the activation of the advanced power activation function (APAF), the AIZNN model is robust in solving the TVTSR problem in the presence of the vanishing and non-vanishing disturbances. We present detailed theoretical analysis to show that, with the AIZNN model, the trajectory of error will converge to zero within a fixed time, and we also calculate the upper bound of the convergence time. Numerical experiments are presented to further verify the robustness of the proposed AIZNN model. Both the theoretical analysis and numerical experiments show that, the proposed AIZNN model provides a novel and noise-tolerant way to solve the TVTSR problem online.

Supported by Newcastle University seed funding “AI for Synthetic Biology and Brain Health research”.

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Author information

Authors and Affiliations

  1. ICOS Research Group, Newcastle University, Newcastle upon Tyne, NE4 5TG, UK

    Jiajie Luo & Jichun Li

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, China

    Jiajie Luo, Lin Xiao & Ping Tan

  3. North of England Robotics Innovation Centre, University of Salford, Manchester, M5 4WT, UK

    Jiguang Li & Wei Yao

Authors
  1. Jiajie Luo
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  2. Lin Xiao
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  3. Ping Tan
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  4. Jiguang Li
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  5. Wei Yao
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  6. Jichun Li
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Corresponding author

Correspondence to Jichun Li .

Editor information

Editors and Affiliations

  1. School of Automation, Central South University, Changsha, China

    Biao Luo

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. School of Automation, Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. School of Electrical Engineering and Telecommunications, UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Luo, J., Xiao, L., Tan, P., Li, J., Yao, W., Li, J. (2024). Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1961. Springer, Singapore. https://doi.org/10.1007/978-981-99-8126-7_9

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  • DOI: https://doi.org/10.1007/978-981-99-8126-7_9

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8125-0

  • Online ISBN: 978-981-99-8126-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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