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

Jiajie Luo; Lin Xiao; Ping Tan; Jiguang Li; Wei Yao; Jichun Li

doi:10.1007/978-981-99-8126-7_9

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

Jiajie Luo, Lin Xiao, Ping Tan, Jiguang Li, Wei Yao, Jichun Li^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VII
Editors	Biao Luo, Long Cheng, Zheng-Guang Wu, Hongyi Li, Chaojie Li
Place of Publication	Singapore
Publisher	Springer
Pages	113-124
Number of pages	12
Volume	7
ISBN (Electronic)	9789819981267
ISBN (Print)	9789819981250
DOIs	https://doi.org/10.1007/978-981-99-8126-7_9
Publication status	Published - 24 Nov 2023
Externally published	Yes
Event	30th International Conference on Neural Information Processing, ICONIP 2023 - Changsha, China Duration: 20 Nov 2023 → 23 Nov 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1961 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	30th International Conference on Neural Information Processing, ICONIP 2023
Country/Territory	China
City	Changsha
Period	20/11/23 → 23/11/23

Keywords

Square root finding
Tensor
Time varying
Zeroing neural network

Access to Document

10.1007/978-981-99-8126-7_9

Cite this

Luo, J., Xiao, L., Tan, P., Li, J., Yao, W., & Li, J. (2023). Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding. In B. Luo, L. Cheng, Z.-G. Wu, H. Li, & C. Li (Eds.), Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VII (Vol. 7, pp. 113-124). (Communications in Computer and Information Science; Vol. 1961 CCIS). Springer. https://doi.org/10.1007/978-981-99-8126-7_9

Luo, Jiajie ; Xiao, Lin ; Tan, Ping et al. / Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding. Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VII. editor / Biao Luo ; Long Cheng ; Zheng-Guang Wu ; Hongyi Li ; Chaojie Li. Vol. 7 Singapore : Springer, 2023. pp. 113-124 (Communications in Computer and Information Science).

@inproceedings{6d2d22c563864c53b9658a5071ca4057,

title = "Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding",

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.",

keywords = "Square root finding, Tensor, Time varying, Zeroing neural network",

author = "Jiajie Luo and Lin Xiao and Ping Tan and Jiguang Li and Wei Yao and Jichun Li",

year = "2023",

month = nov,

day = "24",

doi = "10.1007/978-981-99-8126-7\_9",

language = "English",

isbn = "9789819981250",

volume = "7",

series = "Communications in Computer and Information Science",

publisher = "Springer",

pages = "113--124",

editor = "Biao Luo and Long Cheng and Zheng-Guang Wu and Hongyi Li and Chaojie Li",

booktitle = "Neural Information Processing",

address = "Germany",

note = "30th International Conference on Neural Information Processing, ICONIP 2023 ; Conference date: 20-11-2023 Through 23-11-2023",

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Luo, J, Xiao, L, Tan, P, Li, J, Yao, W & Li, J 2023, Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding. in B Luo, L Cheng, Z-G Wu, H Li & C Li (eds), Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VII. vol. 7, Communications in Computer and Information Science, vol. 1961 CCIS, Springer, Singapore, pp. 113-124, 30th International Conference on Neural Information Processing, ICONIP 2023, Changsha, China, 20/11/23. https://doi.org/10.1007/978-981-99-8126-7_9

Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding. / Luo, Jiajie; Xiao, Lin; Tan, Ping et al.
Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VII. ed. / Biao Luo; Long Cheng; Zheng-Guang Wu; Hongyi Li; Chaojie Li. Vol. 7 Singapore: Springer, 2023. p. 113-124 (Communications in Computer and Information Science; Vol. 1961 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding

AU - Luo, Jiajie

AU - Xiao, Lin

AU - Tan, Ping

AU - Li, Jiguang

AU - Yao, Wei

AU - Li, Jichun

PY - 2023/11/24

Y1 - 2023/11/24

N2 - 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.

AB - 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.

KW - Square root finding

KW - Tensor

KW - Time varying

KW - Zeroing neural network

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U2 - 10.1007/978-981-99-8126-7_9

DO - 10.1007/978-981-99-8126-7_9

M3 - Conference contribution

AN - SCOPUS:85178644477

SN - 9789819981250

VL - 7

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SP - 113

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BT - Neural Information Processing

A2 - Luo, Biao

A2 - Cheng, Long

A2 - Wu, Zheng-Guang

A2 - Li, Hongyi

A2 - Li, Chaojie

PB - Springer

CY - Singapore

T2 - 30th International Conference on Neural Information Processing, ICONIP 2023

Y2 - 20 November 2023 through 23 November 2023

ER -

Luo J, Xiao L, Tan P, Li J, Yao W, Li J. Anti-interference Zeroing Neural Network Model for Time-Varying Tensor Square Root Finding. In Luo B, Cheng L, Wu ZG, Li H, Li C, editors, Neural Information Processing: 30th International Conference, ICONIP 2023, Changsha, China, November 20–23, 2023, Proceedings, Part VII. Vol. 7. Singapore: Springer. 2023. p. 113-124. (Communications in Computer and Information Science). Epub 2023 Nov 13. doi: 10.1007/978-981-99-8126-7_9