Enhanced Results on Sampled-Data Synchronization for Chaotic Neural Networks With Actuator Saturation Using Parameterized Control

This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighti...

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Published inIEEE transaction on neural networks and learning systems Vol. 35; no. 8; pp. 11009 - 11023
Main Authors Jo, Seonghyeon, Kwon, Wookyong, Lee, Sang Jun, Lee, Sangmoon, Jin, Yongsik
Format Journal Article
LanguageEnglish
Published United States IEEE 01.08.2024
Subjects
Actuator saturation
Actuators
Behavioral sciences
Biological neural networks
chaotic neural networks (CNNs)
Control systems
Government
Linear matrix inequalities
linear matrix inequality (LMI)
nonlinearity
sampled-data synchronization control
Synchronization
Online AccessGet full text
ISSN2162-237X
2162-2388
2162-2388
DOI10.1109/TNNLS.2023.3246426

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Abstract This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.
AbstractList This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.
This article investigates a novel sampled-data synchronization controller design method for chaotic neural networks (CNNs) with actuator saturation. The proposed method is based on a parameterization approach which reformulates the activation function as the weighted sum of matrices with the weighting functions. Also, controller gain matrices are combined by affinely transformed weighting functions. The enhanced stabilization criterion is formulated in terms of linear matrix inequalities (LMIs) based on the Lyapunov stability theory and weighting function's information. As shown in the comparison results of the bench marking example, the presented method much outperforms previous methods, and thus the enhancement of the proposed parameterized control is verified.
Author Lee, Sang Jun
Jo, Seonghyeon
Kwon, Wookyong
Jin, Yongsik
Lee, Sangmoon
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Cites_doi 10.1109/ACCESS.2021.3102585
10.1109/TMI.2006.871549
10.1016/j.neucom.2012.11.024
10.1088/1674-1056/21/7/070207
10.1109/TNNLS.2019.2896162
10.1007/s00521-020-05183-0
10.1016/0375-9601(90)90136-C
10.1088/1674-1056/17/2/029
10.1109/TAC.2015.2404271
10.1186/s13662-021-03438-1
10.1109/tnnls.2022.3160627
10.1109/TFUZZ.2017.2686364
10.1109/TNNLS.2020.3027862
10.1002/asjc.2075
10.1007/s12555-018-0678-5
10.1109/72.279188
10.1007/978-0-85729-941-3
10.1109/TFUZZ.2021.3052104
10.1016/j.apm.2018.08.012
10.15388/NA.2018.1.7
10.1109/TCYB.2014.2312004
10.1109/TIE.2016.2544244
10.1016/j.apm.2018.01.046
10.1109/TAC.2017.2670786
10.1007/978-1-4612-0205-9
10.1142/9789814291705_0002
10.1109/TCSI.2002.800842
10.1016/j.cnsns.2008.04.001
10.1109/TNNLS.2020.3045146
10.1109/TNNLS.2019.2943548
10.1109/TFUZZ.2021.3069319
10.1007/s00034-021-01894-4
10.1016/j.physleta.2006.03.069
10.1109/ACCESS.2020.3048170
10.1016/j.neucom.2015.12.055
10.3390/math7080759
10.1109/TCYB.2019.2938217
10.1016/j.neucom.2017.02.063
10.3390/math9111163
10.1016/S0375-9601(03)00387-6
10.1109/ACCESS.2020.3029145
10.1201/9781315220413
10.1063/1.2995852
10.1109/TNN.2007.902958
10.1109/TCSI.2021.3117694
10.1016/j.chaos.2021.111436
10.1016/j.ins.2012.02.007
10.1016/j.automatica.2009.03.004
10.1016/S0893-6080(96)00061-5
10.1109/TAC.2006.878743
10.1017/cbo9780511624216
10.1016/j.jfranklin.2021.02.023
10.1016/j.automatica.2017.04.051
10.1103/PhysRevLett.68.718
10.1109/81.855465
10.1103/PhysRevLett.64.821
10.1007/s12555-018-0245-0
10.1016/j.neucom.2020.09.018
10.1016/j.sysconle.2012.09.003
10.1016/j.neucom.2018.08.090
10.1016/j.automatica.2004.03.003
10.1016/S0167-6911(97)00027-3
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References ref13
ref57
ref12
ref56
ref15
ref59
ref14
ref58
ref53
ref52
ref11
ref55
ref10
ref54
ref17
ref16
ref19
ref18
ref51
ref50
ref46
ref45
ref48
ref47
ref42
ref41
ref44
ref43
ref49
ref8
ref7
ref9
ref4
ref3
ref6
ref5
ref40
ref35
ref34
ref37
ref36
ref31
ref30
ref33
ref32
ref2
ref1
ref39
ref38
ref24
ref23
ref26
ref25
ref20
ref22
ref21
ref28
ref27
ref29
ref60
ref62
ref61
References_xml – ident: ref12
  doi: 10.1109/ACCESS.2021.3102585
– ident: ref2
  doi: 10.1109/TMI.2006.871549
– ident: ref21
  doi: 10.1016/j.neucom.2012.11.024
– ident: ref33
  doi: 10.1088/1674-1056/21/7/070207
– ident: ref26
  doi: 10.1109/TNNLS.2019.2896162
– ident: ref5
  doi: 10.1007/s00521-020-05183-0
– ident: ref8
  doi: 10.1016/0375-9601(90)90136-C
– ident: ref23
  doi: 10.1088/1674-1056/17/2/029
– ident: ref55
  doi: 10.1109/TAC.2015.2404271
– ident: ref14
  doi: 10.1186/s13662-021-03438-1
– ident: ref60
  doi: 10.1109/tnnls.2022.3160627
– ident: ref40
  doi: 10.1109/TFUZZ.2017.2686364
– ident: ref59
  doi: 10.1109/TNNLS.2020.3027862
– ident: ref9
  doi: 10.1002/asjc.2075
– ident: ref49
  doi: 10.1007/s12555-018-0678-5
– ident: ref3
  doi: 10.1109/72.279188
– ident: ref46
  doi: 10.1007/978-0-85729-941-3
– ident: ref61
  doi: 10.1109/TFUZZ.2021.3052104
– ident: ref58
  doi: 10.1016/j.apm.2018.08.012
– ident: ref20
  doi: 10.15388/NA.2018.1.7
– ident: ref47
  doi: 10.1109/TCYB.2014.2312004
– ident: ref54
  doi: 10.1109/TIE.2016.2544244
– ident: ref29
  doi: 10.1016/j.apm.2018.01.046
– ident: ref39
  doi: 10.1109/TAC.2017.2670786
– ident: ref43
  doi: 10.1007/978-1-4612-0205-9
– ident: ref22
  doi: 10.1142/9789814291705_0002
– ident: ref31
  doi: 10.1109/TCSI.2002.800842
– ident: ref32
  doi: 10.1016/j.cnsns.2008.04.001
– ident: ref52
  doi: 10.1109/TNNLS.2020.3045146
– ident: ref34
  doi: 10.1109/TNNLS.2019.2943548
– ident: ref42
  doi: 10.1109/TFUZZ.2021.3069319
– ident: ref6
  doi: 10.1007/s00034-021-01894-4
– ident: ref19
  doi: 10.1016/j.physleta.2006.03.069
– ident: ref17
  doi: 10.1109/ACCESS.2020.3048170
– ident: ref27
  doi: 10.1016/j.neucom.2015.12.055
– ident: ref10
  doi: 10.3390/math7080759
– ident: ref28
  doi: 10.1109/TCYB.2019.2938217
– ident: ref51
  doi: 10.1016/j.neucom.2017.02.063
– ident: ref13
  doi: 10.3390/math9111163
– ident: ref35
  doi: 10.1016/S0375-9601(03)00387-6
– ident: ref11
  doi: 10.1109/ACCESS.2020.3029145
– ident: ref1
  doi: 10.1201/9781315220413
– ident: ref25
  doi: 10.1063/1.2995852
– ident: ref24
  doi: 10.1109/TNN.2007.902958
– ident: ref62
  doi: 10.1109/TCSI.2021.3117694
– ident: ref7
  doi: 10.1016/j.chaos.2021.111436
– ident: ref38
  doi: 10.1016/j.ins.2012.02.007
– ident: ref37
  doi: 10.1016/j.automatica.2009.03.004
– ident: ref18
  doi: 10.1016/S0893-6080(96)00061-5
– ident: ref44
  doi: 10.1109/TAC.2006.878743
– ident: ref4
  doi: 10.1017/cbo9780511624216
– ident: ref41
  doi: 10.1016/j.jfranklin.2021.02.023
– ident: ref57
  doi: 10.1016/j.automatica.2017.04.051
– ident: ref16
  doi: 10.1103/PhysRevLett.68.718
– ident: ref30
  doi: 10.1109/81.855465
– ident: ref15
  doi: 10.1103/PhysRevLett.64.821
– ident: ref53
  doi: 10.1007/s12555-018-0245-0
– ident: ref50
  doi: 10.1016/j.neucom.2020.09.018
– ident: ref45
  doi: 10.1016/j.sysconle.2012.09.003
– ident: ref48
  doi: 10.1016/j.neucom.2018.08.090
– ident: ref36
  doi: 10.1016/j.automatica.2004.03.003
– ident: ref56
  doi: 10.1016/S0167-6911(97)00027-3
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SubjectTerms Actuator saturation
Actuators
Behavioral sciences
Biological neural networks
chaotic neural networks (CNNs)
Control systems
Government
Linear matrix inequalities
linear matrix inequality (LMI)
nonlinearity
sampled-data synchronization control
Synchronization
Title Enhanced Results on Sampled-Data Synchronization for Chaotic Neural Networks With Actuator Saturation Using Parameterized Control
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https://www.ncbi.nlm.nih.gov/pubmed/37028081
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