Time-variant standard Sylvester-conjugate matrix equations are presented as early time-variant versions of the complex conjugate matrix equations. Current solving methods include Con-CZND1 and Con-CZND2 models, both of which use ode45 for continuous model. Given practical computational considerations, discrete these models is also important. Based on Euler-forward formula discretion, Con-DZND1-2i model and Con-DZND2-2i model are proposed. Numerical experiments using step sizes of 0.1 and 0.001. The above experiments show that Con-DZND1-2i model and Con-DZND2-2i model exhibit different neural dynamics compared to their continuous counterparts, such as trajectory correction in Con-DZND2-2i model and the swallowing phenomenon in Con-DZND1-2i model, with convergence affected by step size. These experiments highlight the differences between optimizing sampling discretion errors and space compressive approximation errors in neural dynamics.

翻译：时变标准Sylvester共轭矩阵方程作为复共轭矩阵方程的早期时变形式被提出。现有求解方法包括Con-CZND1与Con-CZND2模型，二者均采用ode45求解连续模型。考虑到实际计算需求，对这些模型进行离散化亦具有重要意义。基于欧拉前向公式离散化方法，本文提出了Con-DZND1-2i模型与Con-DZND2-2i模型。数值实验采用0.1与0.001两种步长进行。实验结果表明：相较于连续模型，Con-DZND1-2i与Con-DZND2-2i模型展现出不同的神经动力学特性，例如Con-DZND2-2i模型的轨迹修正现象与Con-DZND1-2i模型的吞没现象，且收敛性受步长影响。这些实验揭示了神经动力学中优化采样离散误差与空间压缩近似误差之间的差异。