In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) with time-varying delays via quantized sliding-mode algorithm. Quantized Sliding-mode controller is introduced to ensure the slave system can be exponentially synchronized with the host system via the super-twisting algorithm, which has been proved in the main results. Quantization function consists of uniform quantizer and logarithmic quantizer. Simulation results are given with comparisons between two quantizers in the end.

中文翻译：

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通过量化滑模控制具有时变时滞的忆阻神经网络的指数同步。

通过量化滑模算法，研究了具有时变时滞的忆阻神经网络（MNN）的指数同步问题。引入了量化滑模控制器，以确保从属系统可以通过超扭曲算法与主机系统进行指数同步，这在主要结果中得到了证明。量化功能包括统一量化器和对数量化器。最后给出了两个量化器之间的比较，给出了仿真结果。