An Adjustable Memristor Model and Its Application in Small-world Neural Networks

被引：0

作者：

Hu, Xiaofang ^{[1
]}

Feng, Gang ^{[1
]}

Li, Hai ^{[2
]}

Chen, Yiran ^{[2
]}

Duan, Shukai ^{[3
]}

机构：

[1] City Univ Hong Kong, Dept MBE, Kowloon, Hong Kong, Peoples R China

[2] Univ Pittsburgh, Dept ECE, Pittsburgh, PA USA

[3] Southwest Univ, Coll Elect & Informat Engn, Chongqing, Peoples R China

来源：

PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2014年

关键词：

Memristor; PWL window function; Small-world model; function approximation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a novel mathematical model for the TiO2 thin-film memristor device discovered by Hewlett-Packard (HP) labs. Our proposed model considers the boundary conditions and the nonlinear ionic drift effects by using a piecewise linear window function. Four adjustable parameters associated with the window function enable the model to capture complex dynamics of a physical HP memristor. Furthermore, we realize synaptic connections by utilizing the proposed memristor model and provide an implementation scheme for a small-world multilayer neural network. Simulation results are presented to validate the mathematical model and the performance of the neural network in nonlinear function approximation.

引用

页码：7 / 14

页数：8

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