School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Department of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; College of Science and Engineering, Hamad Bin Khalifa University, Qatar.
School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; Department of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
Neural Netw. 2018 Jul;103:142-149. doi: 10.1016/j.neunet.2018.03.015. Epub 2018 Apr 3.
Memristor describes the relationship between charge and flux. Although several window functions for memristors based on the HP linear and nonlinear dopant drift models have been studied, most of them are inadequate to capture the full characteristics of memristors. To address this issue, this paper proposes a unified window function to describe a general memristor with restrictions of its parameters given. Compared with other window functions, the proposed function demonstrates high validity and accuracy. In order to make the simulation results have high consistency with the results of actual circuit, we apply the new window function to the simulation of a memristor-based multilayer neural network (MNN) circuit. The overall accuracy will vary with the change of control parameters in the window function. It implies that the proposed model can guide the design of actual memristor-based circuits.
忆阻器描述了电荷量与磁通量之间的关系。虽然已经研究了几种基于 HP 线性和非线性掺杂漂移模型的忆阻器窗口函数，但大多数函数都无法充分捕捉忆阻器的全部特性。针对这一问题，本文提出了一种统一的窗口函数，可以描述给定参数限制下的一般忆阻器。与其他窗口函数相比，所提出的函数具有较高的有效性和准确性。为了使仿真结果与实际电路的结果具有高度一致性，我们将新的窗口函数应用于基于忆阻器的多层神经网络（MNN）电路的仿真中。整体精度会随窗口函数中控制参数的变化而变化。这意味着所提出的模型可以指导实际基于忆阻器电路的设计。
