Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/64478
Title: Integration-enhanced Zhang neural network for real-time-varying matrix inversion in the presence of various kinds of noises
Authors: Jin, L
Zhang, Y
Li, S 
Keywords: Integration-enhanced Zhang neural network (IEZNN)
Random noise
Real-time-varying matrix inversion
Residual error
Theoretical analysis
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on neural networks and learning systems, 2015, v. 27, no. 12, p. 2615-2627 How to cite?
Journal: IEEE transactions on neural networks and learning systems 
Abstract: Matrix inversion often arises in the fields of science and engineering. Many models for matrix inversion usually assume that the solving process is free of noises or that the denoising has been conducted before the computation. However, time is precious for the real-time-varying matrix inversion in practice, and any preprocessing for noise reduction may consume extra time, possibly violating the requirement of real-time computation. Therefore, a new model for time-varying matrix inversion that is able to handle simultaneously the noises is urgently needed. In this paper, an integration-enhanced Zhang neural network (IEZNN) model is first proposed and investigated for real-time-varying matrix inversion. Then, the conventional ZNN model and the gradient neural network model are presented and employed for comparison. In addition, theoretical analyses show that the proposed IEZNN model has the global exponential convergence property. Moreover, in the presence of various kinds of noises, the proposed IEZNN model is proven to have an improved performance. That is, the proposed IEZNN model converges to the theoretical solution of the time-varying matrix inversion problem no matter how large the matrix-form constant noise is, and the residual errors of the proposed IEZNN model can be arbitrarily small for time-varying noises and random noises. Finally, three illustrative simulation examples, including an application to the inverse kinematic motion planning of a robot manipulator, are provided and analyzed to substantiate the efficacy and superiority of the proposed IEZNN model for real-time-varying matrix inversion.
URI: http://hdl.handle.net/10397/64478
ISSN: 2162-237X
EISSN: 2162-2388
DOI: 10.1109/TNNLS.2015.2497715
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

8
Last Week
0
Last month
Citations as of Oct 10, 2017

WEB OF SCIENCETM
Citations

5
Last Week
0
Last month
Citations as of Oct 15, 2017

Page view(s)

33
Last Week
2
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.