Since the memory-dependent derivative (MDD) was developed in 2011, it has become a new branch of Fractional Calculus which is still in the ascendant nowadays. How to understand MDD and fractional derivative (FD)? What are the advantages and disadvantages for them? How do they behave in Modeling? These questions guide going deep into the illustration of memory effect. Though the FD is defined on an interval, it mainly reflects the local change. Relative to the FD, the physical meaning of MDD is much clearer. The time-delay reflects the duration of memory effect, and the kernel function reflects the dependent weight. The results show that the MDD is more suitable for temporal modeling. In addition, a numerical algorithm for MDD is also developed here.

自2011年开发了依赖内存的导数（MDD）以来，它已成为分数微积分的一个新分支，如今仍在上升。如何理解MDD和分数导数（FD）？它们的优点和缺点是什么？它们在建模中如何表现？这些问题指导您深入了解记忆效应。尽管FD是在一个间隔上定义的，但它主要反映了局部变化。相对于FD，MDD的物理含义更为清晰。时间延迟反映了记忆效应的持续时间，而内核函数反映了相关的权重。结果表明，MDD更适合于时间建模。另外，这里还开发了用于MDD的数值算法。