Dynamic neural network models for time-varying problem solving : a survey on model structures
Hua, Cheng; Cao, Xinwei; Xu, Qian; Liao, Bolin; Li, Shuai (2023-06-27)
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
© The Author(s) 2023. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/.
https://creativecommons.org/licenses/by-nc-nd/4.0/
https://urn.fi/URN:NBN:fi-fe20230913124187
Tiivistelmä
Abstract
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.
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