Synchronization of reaction‐diffusion neural networks with distributed delay via quantized boundary control
Summary This paper investigates the synchronization of reaction‐diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To reduce the communication burden, a novel control strategy combined boundary control and logarithmic quantizer is proposed, and two controllers r...
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| Published in | International journal of adaptive control and signal processing Vol. 37; no. 5; pp. 1166 - 1177 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Bognor Regis
Wiley Subscription Services, Inc
01.05.2023
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| Abstract | Summary
This paper investigates the synchronization of reaction‐diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To reduce the communication burden, a novel control strategy combined boundary control and logarithmic quantizer is proposed, and two controllers respectively subject to constant and adaptive coefficients are carried out. Worth mentioning that the adaptive feedback gain is a matrix in this paper rather than a one‐dimensional variable in most of the existing literatures. Using the Lyapunov functional, the sufficient conditions for delay‐dependent synchronization are obtained through linear matrix inequalities. The effectiveness of the proposed control strategy is illustrated via two examples. |
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| AbstractList | Summary
This paper investigates the synchronization of reaction‐diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To reduce the communication burden, a novel control strategy combined boundary control and logarithmic quantizer is proposed, and two controllers respectively subject to constant and adaptive coefficients are carried out. Worth mentioning that the adaptive feedback gain is a matrix in this paper rather than a one‐dimensional variable in most of the existing literatures. Using the Lyapunov functional, the sufficient conditions for delay‐dependent synchronization are obtained through linear matrix inequalities. The effectiveness of the proposed control strategy is illustrated via two examples. This paper investigates the synchronization of reaction‐diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To reduce the communication burden, a novel control strategy combined boundary control and logarithmic quantizer is proposed, and two controllers respectively subject to constant and adaptive coefficients are carried out. Worth mentioning that the adaptive feedback gain is a matrix in this paper rather than a one‐dimensional variable in most of the existing literatures. Using the Lyapunov functional, the sufficient conditions for delay‐dependent synchronization are obtained through linear matrix inequalities. The effectiveness of the proposed control strategy is illustrated via two examples. |
| Author | Zhang, Xianfu Zhang, Chuan Zhang, Hao Xiang, Han Guo, Yingxin |
| Author_xml | – sequence: 1 givenname: Chuan orcidid: 0000-0003-0564-9434 surname: Zhang fullname: Zhang, Chuan email: zhangchuan@qfnu.edu.cn organization: Shandong University – sequence: 2 givenname: Han surname: Xiang fullname: Xiang, Han organization: Qufu Normal University – sequence: 3 givenname: Xianfu orcidid: 0000-0001-9232-6099 surname: Zhang fullname: Zhang, Xianfu organization: Shandong University – sequence: 4 givenname: Yingxin orcidid: 0000-0001-6003-3945 surname: Guo fullname: Guo, Yingxin organization: Qufu Normal University – sequence: 5 givenname: Hao orcidid: 0000-0003-4281-1461 surname: Zhang fullname: Zhang, Hao organization: Taiyuan University of Technology |
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| Cites_doi | 10.1016/j.jfranklin.2015.06.011 10.1109/TNNLS.2020.2968342 10.1109/TAC.2016.2611559 10.1016/j.neunet.2021.11.012 10.1007/s11063-012-9254-9 10.1007/s11071-011-0278-x 10.1109/TNNLS.2014.2347290 10.1016/j.sysconle.2008.12.007 10.1109/TNN.2011.2128341 10.1109/TSMC.2015.2476491 10.1109/81.802844 10.1109/TCYB.2018.2837090 10.1016/S1751-3243(06)02012-8 10.1002/acs.2917 10.1137/1.9781611970777 10.1051/cocv/2021009 10.1002/acs.2890 10.1016/j.jfranklin.2016.07.019 10.1155/2014/631071 10.1109/TNNLS.2018.2853650 10.1109/TNNLS.2017.2672781 10.1007/s00521-019-04254-1 10.1109/ACCESS.2018.2852489 10.1007/s11571-008-9062-0 10.1002/acs.3478 10.1016/j.amc.2020.125070 10.1016/j.neucom.2016.02.050 10.1109/TSMCB.2012.2235178 10.1007/s11401-012-0754-8 10.1007/s11063-018-9900-y 10.1109/TCYB.2022.3208156 10.1109/TCYB.2019.2960094 10.1109/TCYB.2019.2938217 10.1109/TNNLS.2020.3017171 10.1016/j.automatica.2008.07.005 10.1109/TFUZZ.2022.3145808 |
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| Notes | Funding information China Postdoctoral Science Foundation, Grant/Award Number: 2020M672024; Youth Creative Team Sci‐Tech Program of Shandong Universities, Grant/Award Number: 2019KJI007; National Natural Science Foundation of China, Grant/Award Numbers: 62003189; 61973189; 61702356; Natural Science Foundation of Shandong Province, Grant/Award Number: ZR2021MA043 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
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This paper investigates the synchronization of reaction‐diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To... This paper investigates the synchronization of reaction‐diffusion neural networks (RDNNs) with distributed delay via quantized boundary control. To reduce the... |
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| SubjectTerms | adaptive control Boundary control Delay Diffusion distributed delay Linear matrix inequalities Mathematical analysis Neural networks quantized boundary control reaction‐diffusion neural network Synchronism |
| Title | Synchronization of reaction‐diffusion neural networks with distributed delay via quantized boundary control |
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