Computational modeling study demonstrates fixed-time convergence and noise tolerance in multi-linear M-tensor systems, suggesting improved robustness for complex tensor equations.
Zeroing neural network (ZNN) models are effective methods for solving multi-linear systems. To improve the performance of ZNN models, a new ZNN model is proposed for solving multi-linear systems with M-tensor in this paper. The proposed ZNN model incorporates with a nonlinear activation function. Convergence and robustness of the proposed ZNN model are analyzed. Theoretical results reveal that the proposed ZNN model converges within a fixed time and tolerates different types of noise. Numerical experiments illustrate that the proposed ZNN model is more powerful than some existing ZNN models for solving multi-linear systems with M-tensor.
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Liu et al. (2026) studied this question.
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