Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion

Qiuhong Xiang; Bolin Liao; Lin Xiao; Long Lin; Shuai Li

doi:10.1007/s00500-018-3119-8

Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion

Qiuhong Xiang, Bolin Liao, Lin Xiao, Long Lin, Shuai Li

Research output: Journal article publication › Journal article › Academic research › peer-review

30 Citations (Scopus)

Abstract

In this work, a discrete-time noise-tolerant Zhang neural network (DTNTZNN) model is proposed, developed, and investigated for dynamic matrix pseudoinversion. Theoretical analyses show that the proposed DTNTZNN model is inherently tolerant to noises and can simultaneously deal with different types of noise. For comparison, the discrete-time conventional Zhang neural network (DTCZNN) model is also presented and analyzed to solve the same dynamic problem. Numerical examples and results demonstrate the efficacy and superiority of the proposed DTNTZNN model for dynamic matrix pseudoinversion in the presence of various types of noise.

Original language	English
Pages (from-to)	755-766
Number of pages	12
Journal	Soft Computing
Volume	23
Issue number	3
DOIs	https://doi.org/10.1007/s00500-018-3119-8
Publication status	Published - 13 Feb 2019

Keywords

Discrete time
Dynamic matrix pseudoinverse
Noise tolerant
Numerical examples
Theoretical analysis

ASJC Scopus subject areas

Software
Theoretical Computer Science
Geometry and Topology

More information

10.1007/s00500-018-3119-8

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abstract = "In this work, a discrete-time noise-tolerant Zhang neural network (DTNTZNN) model is proposed, developed, and investigated for dynamic matrix pseudoinversion. Theoretical analyses show that the proposed DTNTZNN model is inherently tolerant to noises and can simultaneously deal with different types of noise. For comparison, the discrete-time conventional Zhang neural network (DTCZNN) model is also presented and analyzed to solve the same dynamic problem. Numerical examples and results demonstrate the efficacy and superiority of the proposed DTNTZNN model for dynamic matrix pseudoinversion in the presence of various types of noise.",

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AU - Xiang, Qiuhong

AU - Liao, Bolin

AU - Xiao, Lin

AU - Lin, Long

AU - Li, Shuai

PY - 2019/2/13

Y1 - 2019/2/13

N2 - In this work, a discrete-time noise-tolerant Zhang neural network (DTNTZNN) model is proposed, developed, and investigated for dynamic matrix pseudoinversion. Theoretical analyses show that the proposed DTNTZNN model is inherently tolerant to noises and can simultaneously deal with different types of noise. For comparison, the discrete-time conventional Zhang neural network (DTCZNN) model is also presented and analyzed to solve the same dynamic problem. Numerical examples and results demonstrate the efficacy and superiority of the proposed DTNTZNN model for dynamic matrix pseudoinversion in the presence of various types of noise.

AB - In this work, a discrete-time noise-tolerant Zhang neural network (DTNTZNN) model is proposed, developed, and investigated for dynamic matrix pseudoinversion. Theoretical analyses show that the proposed DTNTZNN model is inherently tolerant to noises and can simultaneously deal with different types of noise. For comparison, the discrete-time conventional Zhang neural network (DTCZNN) model is also presented and analyzed to solve the same dynamic problem. Numerical examples and results demonstrate the efficacy and superiority of the proposed DTNTZNN model for dynamic matrix pseudoinversion in the presence of various types of noise.

KW - Discrete time

KW - Dynamic matrix pseudoinverse

KW - Noise tolerant

KW - Numerical examples

KW - Theoretical analysis

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ER -

Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion

Abstract

Keywords

ASJC Scopus subject areas

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