Quantized asynchronous dissipative state estimation of jumping neural networks subject to occurring randomly sensor saturations
In this work, we study the quantized asynchronous dissipative state estimation issue for Markov jumping neural networks subject to randomly occurring sensor saturations. The network-induced phenomena (e.g. the sensor saturations and the signal quantization) are assumed to be occurring randomly. A st...
Saved in:
| Published in | Neurocomputing (Amsterdam) Vol. 291; pp. 207 - 214 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
24.05.2018
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | In this work, we study the quantized asynchronous dissipative state estimation issue for Markov jumping neural networks subject to randomly occurring sensor saturations. The network-induced phenomena (e.g. the sensor saturations and the signal quantization) are assumed to be occurring randomly. A stochastic Kronecker delta function is introduced to model such phenomena. The main work aims to design an asynchronous state estimator that assures the underlying error dynamics to be strictly (X,Y,Z)−γ−dissipative. Based on the stochastic Kronecker delta function method and stochastic analysis technique, we establish some conditions for the sake of guaranteeing the existence of the desired state estimator. We finally explain the effectiveness of the proposed design approach by means of a numerical example. |
|---|---|
| AbstractList | In this work, we study the quantized asynchronous dissipative state estimation issue for Markov jumping neural networks subject to randomly occurring sensor saturations. The network-induced phenomena (e.g. the sensor saturations and the signal quantization) are assumed to be occurring randomly. A stochastic Kronecker delta function is introduced to model such phenomena. The main work aims to design an asynchronous state estimator that assures the underlying error dynamics to be strictly (X,Y,Z)−γ−dissipative. Based on the stochastic Kronecker delta function method and stochastic analysis technique, we establish some conditions for the sake of guaranteeing the existence of the desired state estimator. We finally explain the effectiveness of the proposed design approach by means of a numerical example. |
| Author | Huang, Xia Shen, Hao Men, Yunzhe Chen, Bo Wang, Zhen |
| Author_xml | – sequence: 1 givenname: Yunzhe surname: Men fullname: Men, Yunzhe organization: School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243002, China – sequence: 2 givenname: Xia orcidid: 0000-0002-4955-8318 surname: Huang fullname: Huang, Xia email: huangxia_qd@126.com organization: College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China – sequence: 3 givenname: Zhen surname: Wang fullname: Wang, Zhen organization: College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China – sequence: 4 givenname: Hao surname: Shen fullname: Shen, Hao email: haoshen10@gmail.com organization: School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan 243002, China – sequence: 5 givenname: Bo surname: Chen fullname: Chen, Bo organization: College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China |
| BookMark | eNqFkE1LAzEQhoMo2Fb_gYf8gV2TbLsfHgQpfkFBBD2HbDKrWdukZLKVevGvm1pPHvQ0MMzzzswzJofOOyDkjLOcM16e97mDQftVLhivcyZyVvEDMuJ1JbJa1OUhGbFGzDJRcHFMxog9Y7ziohmRz8dBuWg_wFCFW6dfg3d-QGosol2raDdAMaoIFDDaVWp4R31H-2G1tu6FpsVBLVOJ7z68IcWh7UFHGj31Wg8h7IaCcsavlluK4NAHiiomaheFJ-SoU0uE0586Ic8310_zu2zxcHs_v1pkumBlzHTVCAOgdWFEy4CXXTGrW1NPmxkvddG0ZT3tBFezNNEUhRaVEZ0CA13FW6iqYkIu9rk6eMQAndQ2fp8Qg7JLyZncqZS93KuUO5WSCZlUJnj6C16H5CJs_8Mu9xikxzYWgkRtwWkwNiRH0nj7d8AXj1mYZQ |
| CitedBy_id | crossref_primary_10_1016_j_amc_2019_124841 crossref_primary_10_1016_j_jfranklin_2019_06_028 crossref_primary_10_1016_j_jfranklin_2020_04_036 crossref_primary_10_1016_j_neucom_2021_01_131 crossref_primary_10_1109_TCYB_2019_2936827 crossref_primary_10_1016_j_neucom_2019_10_047 crossref_primary_10_1177_0959651818777679 crossref_primary_10_1016_j_neucom_2019_12_018 crossref_primary_10_1016_j_inffus_2023_101814 crossref_primary_10_1016_j_jfranklin_2019_09_031 crossref_primary_10_1016_j_physa_2019_123070 crossref_primary_10_1016_j_amc_2020_125225 crossref_primary_10_1016_j_neucom_2021_02_065 crossref_primary_10_1016_j_amc_2019_124838 crossref_primary_10_1016_j_amc_2019_124715 crossref_primary_10_1016_j_jfranklin_2018_06_030 crossref_primary_10_1016_j_amc_2019_02_081 crossref_primary_10_1016_j_jfranklin_2020_02_011 crossref_primary_10_1109_ACCESS_2020_2968844 crossref_primary_10_1016_j_physa_2019_122437 crossref_primary_10_1007_s12555_019_0934_3 crossref_primary_10_1109_ACCESS_2020_2981721 crossref_primary_10_1016_j_jfranklin_2020_08_037 crossref_primary_10_1016_j_jfranklin_2018_07_011 crossref_primary_10_1016_j_nahs_2020_100906 crossref_primary_10_1016_j_amc_2020_125117 crossref_primary_10_1016_j_jfranklin_2021_01_005 crossref_primary_10_1016_j_amc_2018_05_013 crossref_primary_10_1016_j_amc_2020_125156 crossref_primary_10_1109_TNNLS_2022_3201160 crossref_primary_10_1016_j_neucom_2018_11_011 crossref_primary_10_1177_01423312221143269 crossref_primary_10_1016_j_amc_2022_127538 crossref_primary_10_1016_j_jfranklin_2020_01_021 crossref_primary_10_1016_j_neucom_2020_02_016 crossref_primary_10_1007_s40314_020_01183_x crossref_primary_10_1016_j_jfranklin_2020_04_016 crossref_primary_10_1007_s12555_018_0550_7 crossref_primary_10_1007_s11063_018_9907_4 crossref_primary_10_1016_j_cnsns_2019_04_022 crossref_primary_10_1016_j_amc_2020_125643 crossref_primary_10_1016_j_neucom_2020_09_074 crossref_primary_10_1016_j_amc_2019_124578 crossref_primary_10_1016_j_neucom_2020_03_112 crossref_primary_10_1049_iet_cta_2019_0661 crossref_primary_10_1016_j_jfranklin_2019_11_066 crossref_primary_10_1016_j_jfranklin_2020_03_031 crossref_primary_10_1007_s11071_018_4322_y crossref_primary_10_1016_j_jfranklin_2020_01_005 crossref_primary_10_1016_j_amc_2020_125410 crossref_primary_10_1016_j_amc_2020_125379 crossref_primary_10_1016_j_jfranklin_2019_09_047 crossref_primary_10_1016_j_neucom_2020_09_081 crossref_primary_10_1007_s12555_018_0295_3 crossref_primary_10_1016_j_jfranklin_2024_106685 crossref_primary_10_1109_TSMC_2018_2879985 |
| Cites_doi | 10.1080/00207721.2016.1177130 10.1109/TNNLS.2012.2187926 10.1109/TNNLS.2015.2412676 10.1109/TAC.2017.2774006 10.1016/j.neucom.2012.09.005 10.1109/TNN.2009.2016210 10.1016/j.physleta.2006.03.078 10.1016/j.automatica.2008.03.011 10.1016/j.amc.2017.10.039 10.1109/TSMC.2017.2758381 10.1109/TNNLS.2017.2732240 10.1109/21.278990 10.1016/j.neunet.2008.09.015 10.1109/TNNLS.2016.2516030 10.1007/s00521-015-2079-5 10.1109/TNNLS.2015.2503772 10.1016/j.amc.2015.03.033 10.1016/j.jfranklin.2016.08.020 10.1109/TNNLS.2015.2511196 10.1016/j.amc.2016.10.042 10.1016/j.camwa.2011.04.057 10.1016/j.sysconle.2012.01.005 10.1016/j.jfranklin.2011.11.011 10.1016/j.neucom.2015.01.040 10.1016/j.neucom.2011.05.018 10.1007/s11071-011-0278-x 10.1109/TSMC.2017.2723038 10.1109/TNNLS.2013.2271357 10.12785/amis/072L22 10.1109/TSMC.2017.2708507 10.1109/TNNLS.2012.2236355 10.1016/j.neucom.2012.05.021 10.1109/TSP.2011.2135854 10.1016/j.automatica.2009.09.033 10.1016/j.neucom.2014.02.055 10.1016/j.amc.2012.01.028 10.1016/j.amc.2016.06.039 10.1007/s11071-012-0544-6 10.1016/j.automatica.2013.09.041 |
| ContentType | Journal Article |
| Copyright | 2018 Elsevier B.V. |
| Copyright_xml | – notice: 2018 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.neucom.2018.02.071 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 214 |
| ExternalDocumentID | 10_1016_j_neucom_2018_02_071 S0925231218302388 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of PR China grantid: 61304066; 61473178; 61573008 funderid: https://doi.org/10.13039/501100001809 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN BNPGV CITATION FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW SSH WUQ XPP |
| ID | FETCH-LOGICAL-c306t-c792deecc3d2b0e16f358bd849516c39b684f21a5cc3933c27d2faedef71be773 |
| IEDL.DBID | .~1 |
| ISSN | 0925-2312 |
| IngestDate | Thu Apr 24 22:53:09 EDT 2025 Tue Jul 01 01:46:33 EDT 2025 Fri Feb 23 02:30:23 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Sensor saturations Jumping neural networks Asynchronous dissipative state estimation Signal quantization |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c306t-c792deecc3d2b0e16f358bd849516c39b684f21a5cc3933c27d2faedef71be773 |
| ORCID | 0000-0002-4955-8318 |
| PageCount | 8 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_neucom_2018_02_071 crossref_primary_10_1016_j_neucom_2018_02_071 elsevier_sciencedirect_doi_10_1016_j_neucom_2018_02_071 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2018-05-24 |
| PublicationDateYYYYMMDD | 2018-05-24 |
| PublicationDate_xml | – month: 05 year: 2018 text: 2018-05-24 day: 24 |
| PublicationDecade | 2010 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2018 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Duan, Su, Wu (bib0004) 2012; 97 Shen, Huang, Zhou, Wang (bib0021) 2012; 69 Wang, Xia, Shi (bib0032) 2011; 62 Lakshmanan, Park, Ji, Jung, Nagamani (bib0008) 2012; 70 Zhang, Zhu, Zheng (bib0042) 2016; 27 Zhu, Zhong, Zheng, Zhou (bib0043) 2017 Chen, Shen, Li (bib0002) 2016; 353 Wang, Yao, Shen (bib0026) 2014; 139 Song, Men, Zhou, Shen (bib0023) 2017; 298 Xu, Su, Xu, Wu (bib0037) 2015; 157 Xu, Lu, Shi, Li, Xie (bib0036) 2017 Rakkiyappan, Dharani (bib0013) 2017; 28 Shen, Xu, Lu, Zhou (bib0018) 2012; 349 Dong, Wang, Ho, Gao (bib0003) 2011; 59 Ma, Xu, Zou (bib0012) 2011; 74 Wang, Liu, Liu (bib0028) 2009; 22 Shen, Li, Xu, Sreeram (bib0020) 2017 Tang, Wong (bib0025) 2013; 24 Yan, Zhang, Yang, Zhan, Peng (bib0038) 2017 Wu, Shi, Su, Chu (bib0033) 2014; 50 Xu, Lu, Shi, Tao, Xie (bib0034) 2017 Shen, Wang, Qiao (bib0016) 2017; 28 Wang, Xia, Zhou (bib0031) 2013; 7 Ding, Wang, Shen, Shu (bib0001) 2012; 23 Shen, Wang, Ding, Shu (bib0015) 2013; 24 Yan, Yang, Zhang, Yang, Zhan (bib0039) 2017 Liu, Wang, Liang, Liu (bib0011) 2009; 20 Xu, Lu, Peng, Xie, Xue (bib0035) 2017; 28 Zhang, Shao, Wang, Shen (bib0040) 2012; 218 Rovithakis, Christodoulou (bib0014) 1994; 24 Hu, Wang, Gao, Stergioulas (bib0007) 2012; 61 He, Liu (bib0006) 2013; 103 Fu, de Souza (bib0005) 2009; 45 Wang, Zeng, Park, Zhang, Hayat, Alsaedi (bib0027) 2017; 48 Wang, Xia, Zhou (bib0030) 2013; 2 Liu, Ho, Sun (bib0010) 2008; 44 Liang, Dai, Shen, Wang, Wang, Chen (bib0009) 2018; 321 Wang, Liu, Yu, Liu (bib0029) 2006; 356 Shen, Zhu, Zhang, Park (bib0022) 2017; 28 Shen, Men, Wu, Park (bib0019) 2017 Shen, Huang, Zhou, Wang (bib0017) 2012; 69 Su, Shen (bib0024) 2015; 259 Zhou, Dong, Feng (bib0041) 2017; 293 Yan (10.1016/j.neucom.2018.02.071_sbref0038) 2017 Shen (10.1016/j.neucom.2018.02.071_bib0018) 2012; 349 Xu (10.1016/j.neucom.2018.02.071_sbref0036) 2017 Ding (10.1016/j.neucom.2018.02.071_bib0001) 2012; 23 Shen (10.1016/j.neucom.2018.02.071_sbref0019) 2017 Lakshmanan (10.1016/j.neucom.2018.02.071_bib0008) 2012; 70 Wang (10.1016/j.neucom.2018.02.071_bib0030) 2013; 2 Duan (10.1016/j.neucom.2018.02.071_bib0004) 2012; 97 Liu (10.1016/j.neucom.2018.02.071_bib0010) 2008; 44 Xu (10.1016/j.neucom.2018.02.071_bib0037) 2015; 157 Zhu (10.1016/j.neucom.2018.02.071_sbref0043) 2017 Fu (10.1016/j.neucom.2018.02.071_bib0005) 2009; 45 Hu (10.1016/j.neucom.2018.02.071_bib0007) 2012; 61 Rovithakis (10.1016/j.neucom.2018.02.071_bib0014) 1994; 24 Tang (10.1016/j.neucom.2018.02.071_bib0025) 2013; 24 Wang (10.1016/j.neucom.2018.02.071_bib0029) 2006; 356 Ma (10.1016/j.neucom.2018.02.071_bib0012) 2011; 74 Wang (10.1016/j.neucom.2018.02.071_bib0031) 2013; 7 Liang (10.1016/j.neucom.2018.02.071_bib0009) 2018; 321 Dong (10.1016/j.neucom.2018.02.071_bib0003) 2011; 59 Liu (10.1016/j.neucom.2018.02.071_bib0011) 2009; 20 Shen (10.1016/j.neucom.2018.02.071_bib0017) 2012; 69 Xu (10.1016/j.neucom.2018.02.071_bib0035) 2017; 28 Shen (10.1016/j.neucom.2018.02.071_bib0015) 2013; 24 Song (10.1016/j.neucom.2018.02.071_bib0023) 2017; 298 Yan (10.1016/j.neucom.2018.02.071_sbref0039) 2017 Wang (10.1016/j.neucom.2018.02.071_bib0028) 2009; 22 Zhou (10.1016/j.neucom.2018.02.071_bib0041) 2017; 293 Shen (10.1016/j.neucom.2018.02.071_bib0021) 2012; 69 Wang (10.1016/j.neucom.2018.02.071_bib0026) 2014; 139 Zhang (10.1016/j.neucom.2018.02.071_bib0040) 2012; 218 Wang (10.1016/j.neucom.2018.02.071_bib0027) 2017; 48 Wu (10.1016/j.neucom.2018.02.071_bib0033) 2014; 50 Zhang (10.1016/j.neucom.2018.02.071_bib0042) 2016; 27 Rakkiyappan (10.1016/j.neucom.2018.02.071_bib0013) 2017; 28 Chen (10.1016/j.neucom.2018.02.071_bib0002) 2016; 353 Shen (10.1016/j.neucom.2018.02.071_sbref0020) 2017 Shen (10.1016/j.neucom.2018.02.071_bib0016) 2017; 28 Xu (10.1016/j.neucom.2018.02.071_sbref0034) 2017 Shen (10.1016/j.neucom.2018.02.071_bib0022) 2017; 28 Wang (10.1016/j.neucom.2018.02.071_bib0032) 2011; 62 He (10.1016/j.neucom.2018.02.071_bib0006) 2013; 103 Su (10.1016/j.neucom.2018.02.071_bib0024) 2015; 259 |
| References_xml | – volume: 28 start-page: 1152 year: 2017 end-page: 1163 ident: bib0016 article-title: Event-triggered state estimation for discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements publication-title: IEEE Trans. Neural Netw. Learn. Syst. – year: 2017 ident: bib0019 article-title: Non-fragile publication-title: IEEE Trans. Syst. Man, Cybern., Syst. – volume: 2 start-page: 707 year: 2013 end-page: 724 ident: bib0030 article-title: A numerical method for delayed fractional-order differential equations publication-title: J. Appl. Math. – volume: 27 start-page: 459 year: 2016 end-page: 470 ident: bib0042 article-title: Synchronization and state estimation of a class of hierarchical hybrid neural networks with time-varying delays publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 293 start-page: 617 year: 2017 end-page: 629 ident: bib0041 article-title: Event-triggered communication for synchronization of markovian jump delayed complex networks with partially unknown transition rates publication-title: Appl. Math. Comput. – year: 2017 ident: bib0038 article-title: Event-triggered asynchronous guaranteed cost control for Markov jump discrete-time neural networks with distributed delay and channel fading publication-title: IEEE Trans. Neural Netw. Learn. Syst. – year: 2017 ident: bib0039 article-title: Distributed publication-title: IEEE Trans. Syst. Man Cybern. Syst. – volume: 24 start-page: 400 year: 1994 end-page: 412 ident: bib0014 article-title: Adaptive control of unknown plants using dynamical neural networks publication-title: IEEE Trans. Syst. Man Cybern. – volume: 70 start-page: 1421 year: 2012 end-page: 1434 ident: bib0008 article-title: State estimation of neural networks with time-varying delays and Markovian jumping parameter based on passivity theory publication-title: Nonlinear Dyn. – volume: 20 start-page: 1102 year: 2009 end-page: 1116 ident: bib0011 article-title: Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time delays publication-title: IEEE Trans. Neural Netw. – volume: 44 start-page: 2655 year: 2008 end-page: 2660 ident: bib0010 article-title: Design of publication-title: Automatica – volume: 45 start-page: 2937 year: 2009 end-page: 2945 ident: bib0005 article-title: State estimation for linear discrete-time systems using quantized measurements publication-title: Automatica – volume: 298 start-page: 123 year: 2017 end-page: 132 ident: bib0023 article-title: Event-triggered publication-title: Appl. Math. Comput. – volume: 103 start-page: 87 year: 2013 end-page: 92 ident: bib0006 article-title: Finite-time boundedness of uncertain time-delayed neural network with Markovian jumping parameters publication-title: Neurocomputing – volume: 74 start-page: 3404 year: 2011 end-page: 3411 ident: bib0012 article-title: Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities publication-title: Neurocomputing – volume: 356 start-page: 346 year: 2006 end-page: 352 ident: bib0029 article-title: Exponential stability of delayed recurrent neural networks with Markovian jumping parameters publication-title: Phys. Lett. A – volume: 23 start-page: 725 year: 2012 end-page: 736 ident: bib0001 article-title: state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 259 start-page: 931 year: 2015 end-page: 942 ident: bib0024 article-title: Mixed publication-title: Appl. Math. Comput. – volume: 218 start-page: 7614 year: 2012 end-page: 7621 ident: bib0040 article-title: Reduced-order observer design for the synchronization of the generalized lorenz chaotic systems publication-title: Appl. Math. Comput. – volume: 69 start-page: 473 year: 2012 end-page: 486 ident: bib0021 article-title: Global exponential estimates for uncertain Markovian jump neural networks with reaction-diffusion terms publication-title: Nonlinear Dyn. – volume: 97 start-page: 16 year: 2012 end-page: 21 ident: bib0004 article-title: state estimation of static neural networks with time-varying delay publication-title: Neurocomputing – volume: 28 start-page: 268 year: 2017 end-page: 277 ident: bib0035 article-title: Asynchronous dissipative state estimation for stochastic complex networks with quantized jumping coupling and uncertain measurements publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 48 start-page: 236 year: 2017 end-page: 246 ident: bib0027 article-title: Finite-time control for networked switched linear systems with an event-driven communication approach publication-title: Int. J. Syst. Sci. – volume: 22 start-page: 41 year: 2009 end-page: 48 ident: bib0028 article-title: State estimation for jumping recurrent neural networks with discrete and distributed delays publication-title: Neural Netw. – volume: 69 start-page: 473 year: 2012 end-page: 486 ident: bib0017 article-title: Global exponential estimates for uncertain markovian jump neural networks with reaction-diffusion terms publication-title: Nonlinear Dyn. – volume: 353 start-page: 4583 year: 2016 end-page: 4601 ident: bib0002 article-title: On dissipative filtering over unreliable communication links for stochastic jumping neural networks based on a unified design method publication-title: J. Frankl. Inst. – volume: 28 start-page: 346 year: 2017 end-page: 358 ident: bib0022 article-title: Extended dissipative state estimation for Markov jump neural networks with unreliable links publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 24 start-page: 435 year: 2013 end-page: 447 ident: bib0025 article-title: Distributed synchronization of coupled neural networks via randomly occurring control publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 7 start-page: 525 year: 2013 end-page: 529 ident: bib0031 article-title: A numerical method for delayed fractional-order differential equations: based on g-l definition publication-title: Appl. Math. Inf. Sci. – year: 2017 ident: bib0036 article-title: Finite-time distributed state estimation over sensor networks with round-robin protocol and fading channels publication-title: IEEE Trans. Cybern. – volume: 24 start-page: 2027 year: 2013 end-page: 2037 ident: bib0015 article-title: state estimation for complex networks with uncertain inner coupling and incomplete measurements publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 62 start-page: 1531 year: 2011 end-page: 1539 ident: bib0032 article-title: Analysis of nonlinear dynamics and chaos in a fractional order financial system with time delay publication-title: Comput. Math. Appl. – year: 2017 ident: bib0020 article-title: Slow state variables feedback stabilization for semi-markov jump systems with singular perturbations publication-title: IEEE Trans. Autom. Control – volume: 321 start-page: 450 year: 2018 end-page: 462 ident: bib0009 article-title: synchronization for singularly perturbed complex networks with semi-markov jump topology publication-title: Appl. Math. Comput. – volume: 349 start-page: 1665 year: 2012 end-page: 1680 ident: bib0018 article-title: Passivity-based control for uncertain stochastic jumping systems with mode-dependent round-trip time delays publication-title: J. Frankl. Inst. – volume: 139 start-page: 107 year: 2014 end-page: 113 ident: bib0026 article-title: Dissipativity-based state estimation for Markov jump discrete-time neural networks with unreliable communication links publication-title: Neurocomputing – volume: 50 start-page: 180 year: 2014 end-page: 186 ident: bib0033 article-title: Asynchronous publication-title: Automatica – volume: 157 start-page: 33 year: 2015 end-page: 40 ident: bib0037 article-title: Asynchronous publication-title: Neurocomputing – volume: 61 start-page: 477 year: 2012 end-page: 484 ident: bib0007 article-title: Probability-guaranteed publication-title: Syst. Control Lett. – volume: 59 start-page: 3048 year: 2011 end-page: 3057 ident: bib0003 article-title: Robust filtering for Markovian jump systems with randomly occurring nonlinearities and sensor saturation: the finite-horizon case publication-title: IEEE Trans. Signal Proc. – year: 2017 ident: bib0043 article-title: HMM-based publication-title: IEEE Trans. Syst. Man Cybern. Syst. – volume: 28 start-page: 449 year: 2017 end-page: 462 ident: bib0013 article-title: Sampled-data synchronization of randomly coupled reaction–diffusion neural networks with Markovian jumping and mixed delays using multiple integral approach publication-title: Neural Comput. Appl. – year: 2017 ident: bib0034 article-title: Robust estimation for neural networks with randomly occurring distributed delays and Markovian jump coupling publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 48 start-page: 236 issue: 2 year: 2017 ident: 10.1016/j.neucom.2018.02.071_bib0027 article-title: Finite-time control for networked switched linear systems with an event-driven communication approach publication-title: Int. J. Syst. Sci. doi: 10.1080/00207721.2016.1177130 – volume: 23 start-page: 725 issue: 5 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0001 article-title: H∞ state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2012.2187926 – volume: 27 start-page: 459 issue: 2 year: 2016 ident: 10.1016/j.neucom.2018.02.071_bib0042 article-title: Synchronization and state estimation of a class of hierarchical hybrid neural networks with time-varying delays publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2412676 – year: 2017 ident: 10.1016/j.neucom.2018.02.071_sbref0020 article-title: Slow state variables feedback stabilization for semi-markov jump systems with singular perturbations publication-title: IEEE Trans. Autom. Control doi: 10.1109/TAC.2017.2774006 – volume: 103 start-page: 87 year: 2013 ident: 10.1016/j.neucom.2018.02.071_bib0006 article-title: Finite-time boundedness of uncertain time-delayed neural network with Markovian jumping parameters publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.09.005 – volume: 20 start-page: 1102 issue: 7 year: 2009 ident: 10.1016/j.neucom.2018.02.071_bib0011 article-title: Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time delays publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2009.2016210 – volume: 356 start-page: 346 year: 2006 ident: 10.1016/j.neucom.2018.02.071_bib0029 article-title: Exponential stability of delayed recurrent neural networks with Markovian jumping parameters publication-title: Phys. Lett. A doi: 10.1016/j.physleta.2006.03.078 – year: 2017 ident: 10.1016/j.neucom.2018.02.071_sbref0036 article-title: Finite-time distributed state estimation over sensor networks with round-robin protocol and fading channels publication-title: IEEE Trans. Cybern. – volume: 44 start-page: 2655 issue: 10 year: 2008 ident: 10.1016/j.neucom.2018.02.071_bib0010 article-title: Design of H∞ filter for Markov jumping linear systems with non-accessible mode information publication-title: Automatica doi: 10.1016/j.automatica.2008.03.011 – volume: 321 start-page: 450 year: 2018 ident: 10.1016/j.neucom.2018.02.071_bib0009 article-title: L2−L∞ synchronization for singularly perturbed complex networks with semi-markov jump topology publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2017.10.039 – year: 2017 ident: 10.1016/j.neucom.2018.02.071_sbref0019 article-title: Non-fragile H∞ control for fuzzy Markovian jump systems under fast sampling singular perturbation publication-title: IEEE Trans. Syst. Man, Cybern., Syst. doi: 10.1109/TSMC.2017.2758381 – year: 2017 ident: 10.1016/j.neucom.2018.02.071_sbref0038 article-title: Event-triggered asynchronous guaranteed cost control for Markov jump discrete-time neural networks with distributed delay and channel fading publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2017.2732240 – volume: 24 start-page: 400 issue: 3 year: 1994 ident: 10.1016/j.neucom.2018.02.071_bib0014 article-title: Adaptive control of unknown plants using dynamical neural networks publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/21.278990 – volume: 22 start-page: 41 year: 2009 ident: 10.1016/j.neucom.2018.02.071_bib0028 article-title: State estimation for jumping recurrent neural networks with discrete and distributed delays publication-title: Neural Netw. doi: 10.1016/j.neunet.2008.09.015 – volume: 28 start-page: 1152 issue: 5 year: 2017 ident: 10.1016/j.neucom.2018.02.071_bib0016 article-title: Event-triggered state estimation for discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2516030 – volume: 28 start-page: 449 issue: 3 year: 2017 ident: 10.1016/j.neucom.2018.02.071_bib0013 article-title: Sampled-data synchronization of randomly coupled reaction–diffusion neural networks with Markovian jumping and mixed delays using multiple integral approach publication-title: Neural Comput. Appl. doi: 10.1007/s00521-015-2079-5 – volume: 28 start-page: 268 issue: 2 year: 2017 ident: 10.1016/j.neucom.2018.02.071_bib0035 article-title: Asynchronous dissipative state estimation for stochastic complex networks with quantized jumping coupling and uncertain measurements publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2503772 – volume: 259 start-page: 931 year: 2015 ident: 10.1016/j.neucom.2018.02.071_bib0024 article-title: Mixed H∞/passive synchronization for complex dynamical networks with sampled-data control publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2015.03.033 – volume: 353 start-page: 4583 issue: 17 year: 2016 ident: 10.1016/j.neucom.2018.02.071_bib0002 article-title: On dissipative filtering over unreliable communication links for stochastic jumping neural networks based on a unified design method publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2016.08.020 – volume: 28 start-page: 346 issue: 2 year: 2017 ident: 10.1016/j.neucom.2018.02.071_bib0022 article-title: Extended dissipative state estimation for Markov jump neural networks with unreliable links publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2015.2511196 – year: 2017 ident: 10.1016/j.neucom.2018.02.071_sbref0034 article-title: Robust estimation for neural networks with randomly occurring distributed delays and Markovian jump coupling publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 298 start-page: 123 year: 2017 ident: 10.1016/j.neucom.2018.02.071_bib0023 article-title: Event-triggered H∞ control for networked discrete-time Markov jump systems with repeated scalar nonlinearities publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2016.10.042 – volume: 62 start-page: 1531 issue: 3 year: 2011 ident: 10.1016/j.neucom.2018.02.071_bib0032 article-title: Analysis of nonlinear dynamics and chaos in a fractional order financial system with time delay publication-title: Comput. Math. Appl. doi: 10.1016/j.camwa.2011.04.057 – volume: 61 start-page: 477 issue: 4 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0007 article-title: Probability-guaranteed H∞ finite-horizon filtering for a class of nonlinear time-varying systems with sensor saturations publication-title: Syst. Control Lett. doi: 10.1016/j.sysconle.2012.01.005 – volume: 349 start-page: 1665 issue: 5 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0018 article-title: Passivity-based control for uncertain stochastic jumping systems with mode-dependent round-trip time delays publication-title: J. Frankl. Inst. doi: 10.1016/j.jfranklin.2011.11.011 – volume: 157 start-page: 33 year: 2015 ident: 10.1016/j.neucom.2018.02.071_bib0037 article-title: Asynchronous H∞ filtering for discrete-time Markov jump neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.01.040 – volume: 74 start-page: 3404 issue: 17 year: 2011 ident: 10.1016/j.neucom.2018.02.071_bib0012 article-title: Stability and synchronization for Markovian jump neural networks with partly unknown transition probabilities publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.05.018 – volume: 69 start-page: 473 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0021 article-title: Global exponential estimates for uncertain Markovian jump neural networks with reaction-diffusion terms publication-title: Nonlinear Dyn. doi: 10.1007/s11071-011-0278-x – year: 2017 ident: 10.1016/j.neucom.2018.02.071_sbref0043 article-title: HMM-based H∞ filtering for discrete-time markov jump LPV systems over unreliable communication channels publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2017.2723038 – volume: 24 start-page: 2027 issue: 12 year: 2013 ident: 10.1016/j.neucom.2018.02.071_bib0015 article-title: H∞ state estimation for complex networks with uncertain inner coupling and incomplete measurements publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2013.2271357 – volume: 7 start-page: 525 issue: 2 year: 2013 ident: 10.1016/j.neucom.2018.02.071_bib0031 article-title: A numerical method for delayed fractional-order differential equations: based on g-l definition publication-title: Appl. Math. Inf. Sci. doi: 10.12785/amis/072L22 – year: 2017 ident: 10.1016/j.neucom.2018.02.071_sbref0039 article-title: Distributed H∞ state estimation for a class of filtering networks with time-varying switching topologies and packet losses publication-title: IEEE Trans. Syst. Man Cybern. Syst. doi: 10.1109/TSMC.2017.2708507 – volume: 24 start-page: 435 issue: 3 year: 2013 ident: 10.1016/j.neucom.2018.02.071_bib0025 article-title: Distributed synchronization of coupled neural networks via randomly occurring control publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2012.2236355 – volume: 97 start-page: 16 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0004 article-title: H∞ state estimation of static neural networks with time-varying delay publication-title: Neurocomputing doi: 10.1016/j.neucom.2012.05.021 – volume: 59 start-page: 3048 issue: 7 year: 2011 ident: 10.1016/j.neucom.2018.02.071_bib0003 article-title: Robust filtering for Markovian jump systems with randomly occurring nonlinearities and sensor saturation: the finite-horizon case publication-title: IEEE Trans. Signal Proc. doi: 10.1109/TSP.2011.2135854 – volume: 45 start-page: 2937 issue: 12 year: 2009 ident: 10.1016/j.neucom.2018.02.071_bib0005 article-title: State estimation for linear discrete-time systems using quantized measurements publication-title: Automatica doi: 10.1016/j.automatica.2009.09.033 – volume: 139 start-page: 107 year: 2014 ident: 10.1016/j.neucom.2018.02.071_bib0026 article-title: Dissipativity-based state estimation for Markov jump discrete-time neural networks with unreliable communication links publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.02.055 – volume: 218 start-page: 7614 issue: 14 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0040 article-title: Reduced-order observer design for the synchronization of the generalized lorenz chaotic systems publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2012.01.028 – volume: 2 start-page: 707 year: 2013 ident: 10.1016/j.neucom.2018.02.071_bib0030 article-title: A numerical method for delayed fractional-order differential equations publication-title: J. Appl. Math. – volume: 293 start-page: 617 year: 2017 ident: 10.1016/j.neucom.2018.02.071_bib0041 article-title: Event-triggered communication for synchronization of markovian jump delayed complex networks with partially unknown transition rates publication-title: Appl. Math. Comput. doi: 10.1016/j.amc.2016.06.039 – volume: 70 start-page: 1421 issue: 2 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0008 article-title: State estimation of neural networks with time-varying delays and Markovian jumping parameter based on passivity theory publication-title: Nonlinear Dyn. doi: 10.1007/s11071-012-0544-6 – volume: 69 start-page: 473 year: 2012 ident: 10.1016/j.neucom.2018.02.071_bib0017 article-title: Global exponential estimates for uncertain markovian jump neural networks with reaction-diffusion terms publication-title: Nonlinear Dyn. doi: 10.1007/s11071-011-0278-x – volume: 50 start-page: 180 issue: 1 year: 2014 ident: 10.1016/j.neucom.2018.02.071_bib0033 article-title: Asynchronous l2-l∞ filtering for discrete-time stochastic Markov jump systems with randomly occurred sensor nonlinearities publication-title: Automatica doi: 10.1016/j.automatica.2013.09.041 |
| SSID | ssj0017129 |
| Score | 2.4487486 |
| Snippet | In this work, we study the quantized asynchronous dissipative state estimation issue for Markov jumping neural networks subject to randomly occurring sensor... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 207 |
| SubjectTerms | Asynchronous dissipative state estimation Jumping neural networks Sensor saturations Signal quantization |
| Title | Quantized asynchronous dissipative state estimation of jumping neural networks subject to occurring randomly sensor saturations |
| URI | https://dx.doi.org/10.1016/j.neucom.2018.02.071 |
| Volume | 291 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LS8NAEF5EL158i_XFHryuzT6STY6lWKqiICp4W5LNBio1KU0r1IP-dWfyKAqi4LFhNik7m_lmNt98S8iZl0GOYH3OoJzwmYKclUFWnTAHsTALUGOqkti4uQ2Gj-rqyX9aIf22FwZplU3sr2N6Fa2bK91mNruT0ah770UCqiiOGI_Agw2_Smlc5efvS5oH11zUenvCZ2jdts9VHK_czZEzAiAYVsqdmv8MT18gZ7BFNppckfbqv7NNVly-Qzbbcxho81ruko-7OczP6M2lNC4XuUW9WyjoKX5rrxjTr45WjUMUJTXqXkVaZPQZXAnIRVHTEp6T14zwkpbzBHdn6KyghbW4RwhGgGlp8TJe0BIK32JKS1QErbf79sjj4OKhP2TNwQrMQoUwY1ZHInXgPJmKxHM8yKQfJmkIxRIPrIySIFSZ4LEPFpGUVuhUZLFLXaZ54rSW-2Q1L3J3QKhWynoZyghajjqHkVOxdLGM4U6pH7oOke18GtuojuPhF2PT0sueTe0Fg14wnjDghQ5hy1GTWnXjD3vdusp8Wz0GgOHXkYf_HnlE1vEXMgmEOiars-ncnUCCMktOqxV4StZ6l9fD209BlemN |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDI4GHODCG_EmB65hTdI27REhpgEbEgKk3aI2TaVNo53WDQkO8Nex-0AgIZC4tk5bxYk_O7U_E3LqpOAjGI8zCCc85oLPysCrjpkFW5j6yDFVUmz0b_3uo3s98AYtctHUwmBaZW37K5teWuv6SruezfZkOGzfO6GAKIojxiPwBAtkyYXti20Mzt4-8zy44qIi3BMeQ_Gmfq5M8srsHJNGAAWDkrpT8Z_x6QvmdNbJau0s0vPqezZIy2abZK1pxEDrfblF3u_mMEHDV5vQqHjJDBLeQkRP8Wd7mTL9bGlZOUSRU6MqVqR5SkegS4AuiqSW8J6sSgkvaDGP8XiGznKaG4OHhCAEoJbkT-MXWkDkm09pgZSg1XnfNnnsXD5cdFndWYEZCBFmzKhQJBa0JxMRO5b7qfSCOAkgWuK-kWHsB24qeOSBRCilESoRaWQTmyoeW6XkDlnM8szuEqpc1zgp8ggajkSHoXUjaSMZwZMSL7B7RDbzqU1NO47dL8a6yS8b6UoLGrWgHaFBC3uEfY6aVLQbf8irRlX62_LRgAy_jtz_98gTstx96Pd07-r25oCs4B1MKxDuIVmcTef2CLyVWXxcrsYPoOjrGw |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Quantized+asynchronous+dissipative+state+estimation+of+jumping+neural+networks+subject+to+occurring+randomly+sensor+saturations&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Men%2C+Yunzhe&rft.au=Huang%2C+Xia&rft.au=Wang%2C+Zhen&rft.au=Shen%2C+Hao&rft.date=2018-05-24&rft.issn=0925-2312&rft.volume=291&rft.spage=207&rft.epage=214&rft_id=info:doi/10.1016%2Fj.neucom.2018.02.071&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_neucom_2018_02_071 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |