Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75614
Title: Zeroing neural networks : a survey
Authors: Jin, L 
Li, SA 
Liao, BL
Zhang, ZJ
Keywords: Zeroing neural network
Recurrent neural network
Stability
Numerical algorithms
Redundant manipulators
Robust stability
Issue Date: 2017
Publisher: Elsevier
Source: Neurocomputing, 2017, v. 267, p. 597-604 How to cite?
Journal: Neurocomputing 
Abstract: Using neural networks to handle intractability problems and solve complex computation equations is becoming common practices in academia and industry. It has been shown that, although complicated, these problems can be formulated as a set of equations and the key is to find the zeros of them. Zeroing neural networks (ZNN), as a class of neural networks particularly dedicated to find zeros of equations, have played an indispensable role in the online solution of time-varying problem in the past years and many fruitful research outcomes have been reported in the literatures. The aim of this paper is to provide a comprehensive survey of the research on ZNN5, including continuous-time and discrete-time ZNN models for various problems solving as well as their applications in motion planning and control of redundant manipulators, tracking control of chaotic systems, or even populations control in mathematical biosciences. By considering the fact that real-time performance is highly demanded for time-varying problems in practice, stability and convergence analyses of different continuous-time ZNN models are reviewed in detail in a unified way. For the case of discrete-time problems solving, the procedures on how to discretize a continuous-time ZNN model and the techniques on how to obtain an accuracy solution are summarized. Concluding remarks and future directions of ZNN are pointed out and discussed.
URI: http://hdl.handle.net/10397/75614
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2017.06.030
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