No Cover Image

Journal article 185 views 91 downloads

Proposing, developing and verification of a novel discrete-time zeroing neural network for solving future augmented Sylvester matrix equation

Yang Shi, Long Jin, Shuai Li Orcid Logo, Jipeng Qiang

Journal of the Franklin Institute

Swansea University Author: Shuai Li Orcid Logo

  • shi2020.pdf

    PDF | Accepted Manuscript

    Released under the terms of a Creative Commons Attribution Non-Commercial No Derivatives License (CC-BY-NC-ND).

    Download (1.99MB)

Abstract

In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advanc...

Full description

Published in: Journal of the Franklin Institute
ISSN: 0016-0032
Published: Elsevier BV
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa53642
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: In this paper, a novel discrete-time advance zeroing neural network (DT-AZNN) model is proposed, developed and investigated for solving future augmented Sylvester matrix equation (F-ASME). First of all, based on the advance zeroing neural network (AZNN) design formula, a novel continuous-time advance zeroing neural network (CT-AZNN) model is shown for solving continuous-time augmented Sylvester matrix equation (CT-ASME). Secondly, a recently published discretization formula is further investigated with the optimal sampling gap of the discretization formula proposed. Then, for solving F-ASME, a novel DT-AZNN model is proposed based on the discretization formula. Theoretical analyses on the convergence property and the perturbation suppression performance of the DT-AZNN model are provided. Moreover, comparative numerical experimental results are conducted to prove the effectiveness and robustness of the proposed DT-AZNN model for solving F-ASME.
Keywords: Future augmented Sylvester matrix equation, Zeroing neural network, Discretization formula, Robustness