NDLI: More than Newton iterations generalized from Zhang neural network for constant matrix inversion aided with line-search algorithm

Content Provider	IEEE Xplore Digital Library
Author	Yunong Zhang Dongsheng Guo Chenfu Yi Lingfeng Li Zhende Ke
Copyright Year	2010
Description	Author affiliation: School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510006, China (Yunong Zhang; Dongsheng Guo; Chenfu Yi; Lingfeng Li; Zhende Ke)
Abstract	Since 12 March 2001, Zhang et al have proposed a special class of recurrent neural networks for online time-varying problems solving, especially for matrix inversion. For possible hardware (e.g., digital-circuit) realization, such Zhang neural networks (ZNN) could also be reformulated in the discrete-time form, which incorporates Newton iteration as a special case. In this paper, for constant matrix inversion, we generalize and investigate more discrete-time ZNN models (which could also be termed as ZNN iterations) by using multiple-point backward-difference formulas. For fast convergence to the theoretical inverse, a line-search algorithm is employed to obtain an appropriate step-size value (in each iteration). Computer-simulation results demonstrate the efficacy of the presented new discrete-time ZNN models aided with a line-search algorithm, as compared to Newton iteration.
Starting Page	399
Ending Page	404
File Size	405176
Page Count	6
File Format	PDF
ISBN	9781424451951
ISSN	19483457
DOI	10.1109/ICCA.2010.5524442
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-06-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Recurrent neural networks Equations Signal processing algorithms Problem-solving Design methodology Sun Automatic control Automation Neural network hardware
Content Type	Text
Resource Type	Article

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