Advanced Discrete Generalized-Neurodynamic Model Applied to Solve Discrete Time-Variant Augmented Sylvester Equation with Perturbation Suppression
Yang Shi, Guoqian Liu, Jie Wang, Jiazheng Zhang, Jian Li, Dimitrios K. Gerontitis · 2021
In this paper, an advanced discrete generalized-neurodynamic (A-DGND) model is proposed to solve discrete time-variant augmented Sylvester equation (DTV-ASME) with perturbation suppression. Firstly, we present the discrete time-variant augmented Sylvester matrix equation that can be transformed into a simple matrix-vector problem. Secondly, in the continuous-time environment, for solving the continuous time-variant augmented Sylvester matrix equation (CTV-ASME), an advanced continuous generalized-neurodynamic (A-CGND) model is obtained. Then, based on the four-step discretization formula, an A-DGND model is proposed by discretizing the A-CGND model for solving DTV-ASME with perturbation suppression. Finally, according to the numerical experiment results, the effectiveness and robustness of A-DGND model for solving DTVASME are verified.