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A noise-suppressing discrete-time neural dynamics model for solving time-dependent multi-linear M-tensor equation q
Liu, Mei1,2,3; Wu, Huanmei1,2,3; Shang, Mingsheng3
2023-02-01
摘要Neural dynamics plays an important role in handling various complex problems related to matrices or even tensors, e.g., the multi-linear M-tensor equation investigated in this paper. However, the existing methods for computing the time-dependent multi-linear M-tensor equation bear the following weak-nesses: 1) all of them are under the short-time invariant hypothesis, thereby generating considerable residual errors for time-dependent ones; 2) most of them are depicted in continuous-time form, which can not be directly implemented in the digital equipment; and 3) all of them only consider the noise -free conditions, lacking robustness over truncation errors and round-off errors widely existing in the digital equipment. This paper remedies these three weaknesses by proposing a noise-suppressing discrete-time neural dynamics (NSDTND) model for the time-dependent multi-linear M-tensor equation. Additionally, analyses on the convergence and robustness are shown to demonstrate that the proposed NSDTND model is globally convergent and has a superior immunity to noises. Then, numerical experi-mental verifications and an application to the particle movement are provided to prove the superiority and effectiveness of the proposed NSDTND model for solving time-dependent multi-linear M-tensor equation with noises considered. (c) 2022 Elsevier B.V. All rights reserved.
关键词Multi -linear M -tensor equation Time dependence Noise -suppressing discrete -time neural dynamics (NSDTND)
DOI10.1016/j.neucom.2022.11.071
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号520页码:240-249
通讯作者Shang, Mingsheng(msshang@cigit.ac.cn)
收录类别SCI
WOS记录号WOS:000904659700001
语种英语