DocumentCode :
1797756
Title :
An adjustable memristor model and its application in small-world neural networks
Author :
Xiaofang Hu ; Gang Feng ; Hai Li ; Yiran Chen ; Shukai Duan
Author_Institution :
Dept. of MBE, City Univ. of Hong Kong, Kowloon, China
fYear :
2014
fDate :
6-11 July 2014
Firstpage :
7
Lastpage :
14
Abstract :
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.
Keywords :
function approximation; memristors; multilayer perceptrons; nonlinear functions; piecewise linear techniques; small-world networks; thin film devices; HP labs; Hewlett-Packard labs; TiO2 thin-film memristor device; adjustable memristor model; boundary conditions; nonlinear function approximation; nonlinear ionic drift effects; physical HP memristor; piecewise linear window function; small-world multilayer neural network; Biological system modeling; Computational modeling; Integrated circuit modeling; Mathematical model; Memristors; Numerical models; Semiconductor process modeling; Memristor; PWL window function; Small-world model; function approximation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), 2014 International Joint Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6627-1
Type :
conf
DOI :
10.1109/IJCNN.2014.6889605
Filename :
6889605
Link To Document :
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