期刊
MATHEMATICS
卷 10, 期 11, 页码 -出版社
MDPI
DOI: 10.3390/math10111950
关键词
Yang-Baxter-like matrix equation (YBLME); zeroing neural network (ZNN); dynamical system; Tikhonov regularization
类别
资金
- Ministry of Education, Science, and Technological Development, Republic of Serbia [451-03-68/2022-14/200124]
- Science Fund of the Republic of Serbia [7750185]
This research introduces three novel zeroing neural network models for addressing the time-varying Yang-Baxter-like matrix equation with arbitrary real time-varying input matrices. These models demonstrate effective performance in dealing with the TV-YBLME.
This research introduces three novel zeroing neural network (ZNN) models for addressing the time-varying Yang-Baxter-like matrix equation (TV-YBLME) with arbitrary (regular or singular) real time-varying (TV) input matrices in continuous time. One ZNN dynamic utilizes error matrices directly arising from the equation involved in the TV-YBLME. Moreover, two ZNN models are proposed using basic properties of the YBLME, such as the splitting of the YBLME and sufficient conditions for a matrix to solve the YBLME. The Tikhonov regularization principle enables addressing the TV-YBLME with an arbitrary input real TV matrix. Numerical experiments, including nonsingular and singular TV input matrices, show that the suggested models deal effectively with the TV-YBLME.
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