Memristive Clustering: A Novel Sustainable Parameter Selection Based on Memristive Circuit Model

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Kaikai Qiao;Ben Ma;Lidan Wang;Shukai Duan
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引用次数: 0

Abstract

In recent years, memristors have attracted much attention in the fields of nonvolatile memory, logic operation and neuromorphic computing. As a new type of two-terminal passive electronic component similar to sandwich structure, its main resistance mechanism is the formation and fracture of metal or oxygen vacancy conductive filaments. Traditional clustering algorithms own strong sensitivity to different parameter selection, including partition clustering algorithm and density clustering algorithm. In view of the non-volatile characteristics of memristor and the In-memory computing characteristics of memristive circuit, this paper designs a new memristive clustering paradigm, and further verifies the feasibility and effectiveness of the proposed analog circuit to improve the performance of clustering parameters by exploring the data mining and image segmentation problems of these two types of clustering algorithms.
忆忆聚类:一种基于忆忆电路模型的可持续参数选择方法
近年来,忆阻器在非易失性存储、逻辑运算和神经形态计算等领域受到广泛关注。作为一种类似夹层结构的新型双端无源电子元件,其主要电阻机制是金属或氧空位导电细丝的形成和断裂。传统的聚类算法对不同的参数选择具有较强的敏感性,包括分区聚类算法和密度聚类算法。针对忆阻器的非易失性和忆阻电路的内存计算特性,本文设计了一种新的忆阻聚类范式,并通过探索这两种聚类算法的数据挖掘和图像分割问题,进一步验证了所提出的模拟电路提高聚类参数性能的可行性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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