| Autor: |
Vladislav N. Kovalnogov, Ruslan V. Fedorov, Igor I. Shepelev, Vyacheslav V. Sherkunov, Theodore E. Simos, Spyridon D. Mourtas, Vasilios N. Katsikis |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
AIMS Mathematics, Vol 8, Iss 11, Pp 25966-25989 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2473-6988 |
| DOI: |
10.3934/math.20231323?viewType=HTML |
| Popis: |
Due to its significance in science and engineering, time-varying linear matrix equation (LME) problems have received a lot of attention from scholars. It is for this reason that the issue of finding the minimum-norm least-squares solution of the time-varying quaternion LME (ML-TQ-LME) is addressed in this study. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. In light of that, two new ZNN models are introduced to solve the ML-TQ-LME problem for time-varying quaternion matrices of arbitrary dimension. Two simulation experiments and two practical acoustic source tracking applications show that the models function superbly. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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