Design and Analysis of Quaternion-Valued Neural Networks for Associative Memories

被引：94

作者：

Chen, Xiaofeng ^{[1
]}

Song, Qiankun ^{[1
]}

Li, Zhongshan ^{[2
]}

机构：

[1] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China

[2] Georgia State Univ, Dept Math & Stat, Atlanta, GA 30302 USA

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2018年 / 48卷 / 12期

关键词：

Associative memories; Lyapunov's direct method; quaternion matrix decomposition; quaternion-valued neural networks (QVNNs); semi-discretization technique;

D O I：

10.1109/TSMC.2017.2717866

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper addresses the problem of designing associative memories based on quaternion-valued neural networks (QVNNs). A system designing procedure for QVNNs is developed by employing quaternion matrix decomposition, and a given set of states can be assigned as the equilibrium points of the designed QVNNs. Moreover, some sufficient conditions for the asymptotic stability of the equilibrium points are obtained via Lyapunov's direct method. Numerical simulations manifest that the constructed QVNNs work efficiently on storing and retrieving blurred gray-scale and true color images.

引用

页码：2305 / 2314

页数：10

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