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Soft Computing

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08.03.2018 | Foundations

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

verfasst von: Qiuhong Xiang, Bolin Liao, Lin Xiao, Long Lin, Shuai Li

Erschienen in: Soft Computing | Ausgabe 3/2019

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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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Titel: Discrete-time noise-tolerant Zhang neural network for dynamic matrix pseudoinversion
verfasst von: Qiuhong Xiang
Bolin Liao
Lin Xiao
Long Lin
Shuai Li
Publikationsdatum: 08.03.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 3/2019
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-018-3119-8

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