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It is well-known that, the classical gradient-descent-based neural network (CGNN) model is used widely for the time-invariant problem solving. However, it is an extremely common problem for the time varying cases in the practical engineering applications, while the CGNN effective neural solver for the time-variant problems. For this reason, in this article, an adaptive GNN (AGNN) is presented for the linear time variant matrix equation (LTVME) on-line solving based on Lyapunov theory. Theoretical analysis already verified that the presented AGNN model could achieve the correct state solution effectively. The simulated experiment results further validate that the state solution of the AGNN model could be convergent to the correct solution of the solved time variant problems in theory. [ABSTRACT FROM AUTHOR]

It is well-known that, the classical gradient-descent-based neural network (CGNN) model is used widely for the time-invariant problem solving. However, it is an extremely common problem for the time varying cases in the practical engineering applications, while the CGNN effective neural solver for the time-variant problems. For this reason, in this article, an adaptive GNN (AGNN) is presented for the linear time variant matrix equation (LTVME) on-line solving based on Lyapunov theory. Theoretical analysis already verified that the presented AGNN model could achieve the correct state solution effectively. The simulated experiment results further validate that the state solution of the AGNN model could be convergent to the correct solution of the solved time variant problems in theory. [ABSTRACT FROM AUTHOR]

During the process of the general practical applications and scientific researches, time varying issues are an inescapable challenge to be tackled. In this article, a pre-defined finite-time zeroing neural networks (PDFT-ZNN) is devoted to solve the linear time-variant matrix equation (TVME) E (t) X (t) G (t) = D (t) within a finite time (FT), which contrasts with the general ZNN (G-ZNN) models with relatively long global convergence time. Moreover, the proposed PDFT-ZNN model's convergence time could be calculated in advance by adhering to the designed system parameters; this has nothing to do with the model's initial state. Additionally, after the convergence analysis, if the solution error is relative small, the simple and effective method is to introduce a linear item to accelerate the convergence speed, in comparison with only the power-type ZNN models. Theory-based analysis and simulation-based results further validate that, the neural state solved by the presented FT-ZNN model can reach the theory-based solution of E (t) X (t) G (t) = D (t) within the pre-defined finite time. • Th presented PDFT-ZNN model can predict the convergence time for the control system in advance. • A detailed theoretical analysis are carried out for the proposed PDFT-ZNN model. • The illustrative example is computed and examined for the proposed PDFT-ZNN model. [ABSTRACT FROM AUTHOR]