University of Oulu

C. Hua, X. Cao, Q. Xu, B. Liao and S. Li, "Dynamic Neural Network Models for Time-Varying Problem Solving: A Survey on Model Structures," in IEEE Access, vol. 11, pp. 65991-66008, 2023, doi: 10.1109/ACCESS.2023.3290046

Dynamic neural network models for time-varying problem solving : a survey on model structures

Saved in:
Author: Hua, Cheng1; Cao, Xinwei2; Xu, Qian1;
Organizations: 1College of Computer Science and Engineering, Jishou University, Jishou, China
2School of Business, Jiangnan University, Wuxi, China
3Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
4VTT–Technology Research Center of Finland, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.2 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2023
Publish Date: 2023-09-13


In recent years, neural networks have become a common practice in academia for handling complex problems. Numerous studies have indicated that complex problems can generally be formulated as a single or a set of time-varying equations. Dynamic neural networks, as powerful tools for processing time-varying problems, play an essential role in their online solution. This paper reviews recent advances in real-valued, complex-valued, and noise-tolerant dynamic neural networks for solving various time-varying problems, discusses the finite-time convergence, fixed/varying parameters, and noise tolerance properties of dynamic neural network models. This review is highly relevant for researchers and novices interested in using dynamic neural networks to solve time-varying problems.

see all

Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 11
Pages: 65991 - 66008
DOI: 10.1109/ACCESS.2023.3290046
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the National Natural Science Foundation of China under Grant 62066015 and Grant 62006095.
Copyright information: © The Author(s) 2023. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see