ZNN Models for Computing Matrix Inverse Based on Hyperpower Iterative Methods

被引：14

作者：

Stojanovic, Igor ^{[1
]}

Stanimirovic, Predrag S. ^{[2
]}

Zivkovic, Ivan S. ^{[3
]}

Gerontitis, Dimitrios ^{[4
]}

Wang, Xue-Zhong ^{[5
]}

机构：

[1] Goce Delcev Univ, Fac Comp Sci, Goce Delcev 89, Stip 2000, Macedonia

[2] Univ Nis, Fac Sci & Math, Visegradska 33, Nish 18000, Serbia

[3] Serbian Acad Arts & Sci, Math Inst, Kneza Mihaila 36, Belgrade 11001, Serbia

[4] Aristotele Panepistim, Thessalonikis, Greece

[5] Fudan Univ, Sch Math Sci, Shanghai 200433, Peoples R China

来源：

FILOMAT | 2017年 / 31卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Zhang neural network; matrix inverse; convergence; time-varying complex matrix; iterative methods; RECURRENT NEURAL-NETWORK; MOORE-PENROSE INVERSE; REPRESENTATION;

D O I：

10.2298/FIL1710999S

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

Our goal is to investigate and exploit an analogy between the scaled hyperpower family (SHPI family) of iterative methods for computing the matrix inverse and the discretization of Zhang Neural Network (ZNN) models. A class of ZNN models corresponding to the family of hyperpower iterative methods for computing generalized inverses is defined on the basis of the discovered analogy. The Simulink implementation in Matlab of the introduced ZNN models is described in the case of scaled hyperpower methods of the order 2 and 3. Convergence properties of the proposed ZNN models are investigated as well as their numerical behavior.

引用

页码：2999 / 3014

页数：16

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