International Journal of Computational Intelligence Systems

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Volume 13, Issue 1, 2020, Pages 663 - 671

A Neural Network for Moore–Penrose Inverse of Time-Varying Complex-Valued Matrices

Authors
Yiyuan Chai1, Haojin Li2, Defeng Qiao2, Sitian Qin2, *, ORCID, Jiqiang Feng1
1Shenzhen Key Laboratory of Advanced Machine Learning and Application, College of Mathematics and Statistics, Shenzhen University, Shenzhen, 518060, China
2Department of Mathematics, Harbin Institute of Technology, Weihai, 264209, China
*Corresponding author. Email: qinsitian@163.com
Corresponding Author
Sitian Qin
Received 1 March 2020, Accepted 17 May 2020, Available Online 17 June 2020.
DOI
10.2991/ijcis.d.200527.001How to use a DOI?
Keywords
Zhang neural network; Moore–Penrose inverse; Finite-time convergence; Noise suppression
Abstract

The Moore–Penrose inverse of a matrix plays a very important role in practical applications. In general, it is not easy to immediately solve the Moore–Penrose inverse of a matrix, especially for solving the Moore–Penrose inverse of a complex-valued matrix in time-varying situations. To solve this problem conveniently, in this paper, a novel Zhang neural network (ZNN) with time-varying parameter that accelerates convergence is proposed, which can solve Moore–Penrose inverse of a matrix over complex field in real time. Analysis results show that the state solutions of the proposed model can achieve super convergence in finite time with weighted sign-bi-power activation function (WSBP) and the upper bound of the convergence time is calculated. A related noise-tolerance model which possesses finite-time convergence property is proved to be more efficient in noise suppression. At last, numerical simulation illustrates the performance of the proposed model as well.

Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
13 - 1
Pages
663 - 671
Publication Date
2020/06/17
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.200527.001How to use a DOI?
Copyright
© 2020 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Yiyuan Chai
AU  - Haojin Li
AU  - Defeng Qiao
AU  - Sitian Qin
AU  - Jiqiang Feng
PY  - 2020
DA  - 2020/06/17
TI  - A Neural Network for Moore–Penrose Inverse of Time-Varying Complex-Valued Matrices
JO  - International Journal of Computational Intelligence Systems
SP  - 663
EP  - 671
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200527.001
DO  - 10.2991/ijcis.d.200527.001
ID  - Chai2020
ER  -