Citation

Shi, Shi and Jiang, Chao and Zhao, Ruxin and Gerontitis, Dimitrios K. and Yeong, Wai Chung and Shi, Yang (2026) A systematic review of zeroing neural networks: Modeling, analysis, and applications. Neurocomputing, 703. p. 134749. ISSN 09252312

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Abstract

The zeroing neural network (ZNN), a recurrent neural network specifically designed for time-varying problems, has become an important tool in the fields of science and engineering for solving complex dynamic challenges. With its distinctive error evolution, ZNN is capable of effectively solving time-varying problems and suppressing residual errors in dynamic systems. This paper presents a comprehensive review of recent research progress on ZNN. We systematically summarize the basic models and noise-tolerant variants of continuous-time ZNN, as well as discrete-time ZNN models and common discretization techniques. Moreover, we analyze the core properties of ZNN models, including convergence and stability, together with the roles of activation functions and key parameters. Applications in robotic control, chaotic system synchronization, and other related fields are also discussed, demonstrating the great potential and promising future of ZNN in practical engineering scenarios.

Item Type:	Article
Uncontrolled Keywords:	Zeroing neural network, Convergence
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Sep 2026 06:49
Last Modified:	02 Sep 2026 06:49
URII:	http://shdl.mmu.edu.my/id/eprint/16534

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