Predefined-time ZNN model with noise reduction for solving quadratic programming and its application to binary assignment problem in logistics

Bolin Liao; Jinsha Xu; Cheng Hua; Tinglei Wang; Shuai Li

doi:10.1007/s11227-025-07694-w

Predefined-time ZNN model with noise reduction for solving quadratic programming and its application to binary assignment problem in logistics

Bolin Liao
, Jinsha Xu
, Cheng Hua
, Tinglei Wang
, Shuai Li^*

^*Corresponding author for this work

VTT Technical Research Centre of Finland

Jishou University
University of Oulu
VTT (former employee or external)

Research output: Contribution to journal › Article › Scientific › peer-review

11 Citations (Scopus)

Abstract

Zeroing neural networks (ZNNs), a specialized class of recurrent neural networks, have demonstrated remarkable effectiveness in matrix computation and dynamic optimization problems due to their inherent parallel computing capabilities. In this paper, a predefined-time and noise reduction ZNN (PTNRZNN) model is proposed for solving convex quadratic programming problems with equality and inequality constraints. Additionally, a new activation function is proposed, demonstrating enhanced accelerated convergence and noise reduction performance compared to previous models. The convergence and robustness of the PTNRZNN model are effectively proven through theoretical assessment. Furthermore, the performance of the PTNRZNN model is further validated through simulation experiments. Finally, the PTNRZNN model is applied to the binary assignment problem in logistics (BAPL), yielding optimized results with an error margin as low as 10-2 compared to theoretical values. The strong robustness of the method makes it an excellent performer in solving BAPL under noise interference.

Original language	English
Article number	1228
Journal	Journal of Supercomputing
Volume	81
Issue number	12
DOIs	https://doi.org/10.1007/s11227-025-07694-w
Publication status	Published - Aug 2025
MoE publication type	A1 Journal article-refereed

Funding

Open Access funding provided by University of Oulu (including Oulu University Hospital). This work was supported in part by the National Natural Science Foundation of China under Grant No. 62466019, the Regional Joint Fund of the Hunan Provincial Natural Science Foundation under Grant No. 2024JJ7549 and the Research Foundation of Education Bureau of Hunan Province, China, under Grant No. 24C1236.

Keywords

Binary assignment problem
Constraints quadratic programming
Predefined-time
Zeroing neural network

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10.1007/s11227-025-07694-wLicence: CC BY

Cite this

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abstract = "Zeroing neural networks (ZNNs), a specialized class of recurrent neural networks, have demonstrated remarkable effectiveness in matrix computation and dynamic optimization problems due to their inherent parallel computing capabilities. In this paper, a predefined-time and noise reduction ZNN (PTNRZNN) model is proposed for solving convex quadratic programming problems with equality and inequality constraints. Additionally, a new activation function is proposed, demonstrating enhanced accelerated convergence and noise reduction performance compared to previous models. The convergence and robustness of the PTNRZNN model are effectively proven through theoretical assessment. Furthermore, the performance of the PTNRZNN model is further validated through simulation experiments. Finally, the PTNRZNN model is applied to the binary assignment problem in logistics (BAPL), yielding optimized results with an error margin as low as 10-2 compared to theoretical values. The strong robustness of the method makes it an excellent performer in solving BAPL under noise interference.",

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author = "Bolin Liao and Jinsha Xu and Cheng Hua and Tinglei Wang and Shuai Li",

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AU - Liao, Bolin

AU - Xu, Jinsha

AU - Hua, Cheng

AU - Wang, Tinglei

AU - Li, Shuai

PY - 2025/8

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N2 - Zeroing neural networks (ZNNs), a specialized class of recurrent neural networks, have demonstrated remarkable effectiveness in matrix computation and dynamic optimization problems due to their inherent parallel computing capabilities. In this paper, a predefined-time and noise reduction ZNN (PTNRZNN) model is proposed for solving convex quadratic programming problems with equality and inequality constraints. Additionally, a new activation function is proposed, demonstrating enhanced accelerated convergence and noise reduction performance compared to previous models. The convergence and robustness of the PTNRZNN model are effectively proven through theoretical assessment. Furthermore, the performance of the PTNRZNN model is further validated through simulation experiments. Finally, the PTNRZNN model is applied to the binary assignment problem in logistics (BAPL), yielding optimized results with an error margin as low as 10-2 compared to theoretical values. The strong robustness of the method makes it an excellent performer in solving BAPL under noise interference.

AB - Zeroing neural networks (ZNNs), a specialized class of recurrent neural networks, have demonstrated remarkable effectiveness in matrix computation and dynamic optimization problems due to their inherent parallel computing capabilities. In this paper, a predefined-time and noise reduction ZNN (PTNRZNN) model is proposed for solving convex quadratic programming problems with equality and inequality constraints. Additionally, a new activation function is proposed, demonstrating enhanced accelerated convergence and noise reduction performance compared to previous models. The convergence and robustness of the PTNRZNN model are effectively proven through theoretical assessment. Furthermore, the performance of the PTNRZNN model is further validated through simulation experiments. Finally, the PTNRZNN model is applied to the binary assignment problem in logistics (BAPL), yielding optimized results with an error margin as low as 10-2 compared to theoretical values. The strong robustness of the method makes it an excellent performer in solving BAPL under noise interference.

KW - Binary assignment problem

KW - Constraints quadratic programming

KW - Predefined-time

KW - Zeroing neural network

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Predefined-time ZNN model with noise reduction for solving quadratic programming and its application to binary assignment problem in logistics

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