Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/75614
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Jin, L | - |
dc.creator | Li, SA | - |
dc.creator | Liao, BL | - |
dc.creator | Zhang, ZJ | - |
dc.date.accessioned | 2018-05-10T02:54:12Z | - |
dc.date.available | 2018-05-10T02:54:12Z | - |
dc.identifier.issn | 0925-2312 | - |
dc.identifier.uri | http://hdl.handle.net/10397/75614 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ ) | en_US |
dc.rights | The following publication Jin, L., Li, S., Liao, B., & Zhang, Z. (2017). Zeroing neural networks: A survey. Neurocomputing, 267, 597-604 is available at https://doi.org/10.1016/j.neucom.2017.06.030 | en_US |
dc.subject | Zeroing neural network | en_US |
dc.subject | Recurrent neural network | en_US |
dc.subject | Stability | en_US |
dc.subject | Numerical algorithms | en_US |
dc.subject | Redundant manipulators | en_US |
dc.subject | Robust stability | en_US |
dc.title | Zeroing neural networks : a survey | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 597 | - |
dc.identifier.epage | 604 | - |
dc.identifier.volume | 267 | - |
dc.identifier.doi | 10.1016/j.neucom.2017.06.030 | - |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Neurocomputing, 2017, v. 267, p. 597-604 | - |
dcterms.isPartOf | Neurocomputing | - |
dcterms.issued | 2017 | - |
dc.identifier.isi | WOS:000409285400054 | - |
dc.identifier.eissn | 1872-8286 | - |
dc.description.validate | 201805 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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Jin_Zeroing_Neural_Networks.pdf | 568.37 kB | Adobe PDF | View/Open |
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