Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey

Derui Ding; Qing-Long Han; Xiaohua Ge

Kybernetika (2020)

  • Volume: 56, Issue: 1, page 5-34
  • ISSN: 0023-5954

Abstract

top
Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.

How to cite

top

Ding, Derui, Han, Qing-Long, and Ge, Xiaohua. "Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey." Kybernetika 56.1 (2020): 5-34. <http://eudml.org/doc/297098>.

@article{Ding2020,
abstract = {Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.},
author = {Ding, Derui, Han, Qing-Long, Ge, Xiaohua},
journal = {Kybernetika},
keywords = {distributed filtering; sensor networks; non-Gaussian noises; network-induced phenomena; communication protocols},
language = {eng},
number = {1},
pages = {5-34},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey},
url = {http://eudml.org/doc/297098},
volume = {56},
year = {2020},
}

TY - JOUR
AU - Ding, Derui
AU - Han, Qing-Long
AU - Ge, Xiaohua
TI - Distributed filtering of networked dynamic systems with non-gaussian noises over sensor networks: A survey
JO - Kybernetika
PY - 2020
PB - Institute of Information Theory and Automation AS CR
VL - 56
IS - 1
SP - 5
EP - 34
AB - Sensor networks are regarded as a promising technology in the field of information perception and processing owing to the ease of deployment, cost-effectiveness, flexibility, as well as reliability. The information exchange among sensors inevitably suffers from various network-induced phenomena caused by the limited resource utilization and complex application scenarios, and thus is required to be governed by suitable resource-saving communication mechanisms. It is also noteworthy that noises in system dynamics and sensor measurements are ubiquitous and in general unknown but can be bounded, rather than follow specific Gaussian distributions as assumed in Kalman-type filtering. Particular attention of this paper is paid to a survey of recent advances in distributed filtering of networked dynamic systems with non-Gaussian noises over sensor networks. First, two types of widely employed structures of distributed filters are reviewed, the corresponding analysis is systematically addressed, and some interesting results are provided. The inherent purpose of adding consensus terms into the distributed filters is profoundly disclosed. Then, some representative models characterizing various network-induced phenomena are reviewed and their corresponding analytical strategies are exhibited in detail. Furthermore, recent results on distributed filtering with non-Gaussian noises are sorted out in accordance with different network-induced phenomena and system models. Another emphasis is laid on recent developments of distributed filtering with various communication scheduling, which are summarized based on the inherent characteristics of their dynamic behavior associated with mathematical models. Finally, the state-of-the-art of distributed filtering and challenging issues, ranging from scalability, security to applications, are raised to guide possible future research.
LA - eng
KW - distributed filtering; sensor networks; non-Gaussian noises; network-induced phenomena; communication protocols
UR - http://eudml.org/doc/297098
ER -

References

top
  1. Aazam, M., Zeadally, S., Harras, K. A., 10.1109/mcom.2018.1700707, IEEE Comm. Magazine 56, (2018), 5, 2018, 46-52. MR3843414DOI10.1109/mcom.2018.1700707
  2. Ahmad, F., Rasool, A., Ozsoy, E., Rajasekar, S., Sabanovic, A., Elitaş, M., 10.1016/j.rser.2017.06.071, Renewable Sustainable Energy Rev. 81 (2018), 2659-2671. DOI10.1016/j.rser.2017.06.071
  3. Chen, W., Ding, D., Dong, H., Wei, G., 10.1109/tsmc.2019.2905253, IEEE Trans. Systems Man Cybernet.: Systems 49 (2019), 8, 1688-1697. DOI10.1109/tsmc.2019.2905253
  4. Chen, W., Ding, D., Ge, X., Han, Q.-L., Wei, G., 10.1109/tcyb.2018.2885567, IEEE Trans. Cybernet. 50 (2020), 4, 1372-1382. DOI10.1109/tcyb.2018.2885567
  5. Chen, Y., Wang, Z., Yuan, Y., Date, P., 10.1109/tcyb.2018.2852290, IEEE Trans. Cybernet. 50 (2018), 1, 2-14. DOI10.1109/tcyb.2018.2852290
  6. Ding, D., Han, Q.-L., Wang, Z., Ge, X., 10.1109/tsmc.2019.2960541, IEEE Trans. Systems Man Cybernet.: Systems. DOI10.1109/tsmc.2019.2960541
  7. Ding, D., Han, Q.-L., Wang, Z., Ge, X., 10.1109/tii.2019.2905295, IEEE Trans. Industr. Inform. 15 (2019), 5, 2483-2499. DOI10.1109/tii.2019.2905295
  8. Ding, D., Wang, Z., Dong, H., Shu, H., 10.1016/j.automatica.2012.05.070, Automatica 48 (2012), 8, 1575-1585. MR2950405DOI10.1016/j.automatica.2012.05.070
  9. Ding, D., Wang, Z., Han, Q.-L., 10.1016/j.automatica.2019.04.025, Automatica 106 (2019), 221-229. MR3952583DOI10.1016/j.automatica.2019.04.025
  10. Ding, D., Wang, Z., Han, Q.-L., 10.1109/tac.2019.2934389, IEEE Trans. Automat. Control 65 (2020), 4, 1792-1799. MR4052856DOI10.1109/tac.2019.2934389
  11. Ding, D., Wang, Z., Han, Q.-L., 10.1109/tcyb.2019.2917543, IEEE Trans. Cybernet. DOI10.1109/tcyb.2019.2917543
  12. Ding, D., Wang, Z., Han, Q.-L., Wei, G., 10.1109/tcyb.2018.2827037, IEEE Trans. Cybernet. 49 (2019), 6, 2372-2384. DOI10.1109/tcyb.2018.2827037
  13. Ding, D., Wang, Z., Ho, D. W. C., Wei, G., 10.1016/j.automatica.2016.12.026, Automatica 78 (2017), 231-240. MR3614098DOI10.1016/j.automatica.2016.12.026
  14. Ding, D., Wang, Z., Lam, J., Shen, B., 10.1109/tac.2014.2380671, IEEE Trans. Automat. Control 60 (2015), 9, 2488-2493. MR3393143DOI10.1109/tac.2014.2380671
  15. Ding, D., Wang, Z., Shen, B., Shu, H., 10.1109/tnnls.2012.2187926, IEEE Trans. Neural Networks Learning Systems 23 (2012), 5, 725-736. DOI10.1109/tnnls.2012.2187926
  16. Ding, L., Han, Q.-L., Zhang, X.-M., 10.1109/tii.2018.2884494, IEEE Trans. Industr. Inform. 15 (2019), 7, 3910-3922. DOI10.1109/tii.2018.2884494
  17. Ding, L., Han, Q.-L., Ge, X., Zhang, X.-M., 10.1109/tcyb.2017.2771560, IEEE Trans. Cybernet. 48 (2018), 4, 1110-1123. MR3554944DOI10.1109/tcyb.2017.2771560
  18. Ding, L., Han, Q.-L., Wang, L., Sindi, E., 10.1109/tii.2018.2799239, IEEE Trans. Industr. Inform. 14 (2018), 9, 3924-3935. DOI10.1109/tii.2018.2799239
  19. Dong, H., Wang, Z., Gao, H., 10.1109/tsp.2012.2190599, IEEE Trans. Signal Process. 60 (2012), 6, 3164-3173. MR2924079DOI10.1109/tsp.2012.2190599
  20. Girard, A., 10.1109/tac.2014.2366855, IEEE Trans. Cybernet. 60 (2015), 7, 1992-1997. MR3365092DOI10.1109/tac.2014.2366855
  21. Ge, X., Han, Q.-L., 10.1016/j.ins.2014.08.047, Inform. Sci. 291 (2015), 128-142. MR3264405DOI10.1016/j.ins.2014.08.047
  22. Ge, X., Han, Q.-L., 10.1109/tie.2017.2701778, IEEE Trans. Industr. Electron. 64 (2017), 10, 8118-8127. DOI10.1109/tie.2017.2701778
  23. Ge, X., Han, Q.-L., Wang, Z., 10.1109/tcyb.2017.2789296, IEEE Trans. Cybernet. 49 (2019), 4, 1148-1159. DOI10.1109/tcyb.2017.2789296
  24. Ge, X., Han, Q.-L., Wang, Z., 10.1109/tcyb.2017.2769722, IEEE Trans. Cybernet. 49 (2019), 1, 171-183. DOI10.1109/tcyb.2017.2769722
  25. Ge, X., Han, Q.-L., Zhang, X.-M., Ding, L., Yang, F., 10.1109/tcyb.2019.2917179, IEEE Trans. Cybernet. 50 (2020), 3, 1306-1320. DOI10.1109/tcyb.2019.2917179
  26. Ge, X., Han, Q.-L., Zhang, X.-M., Ding, D., Yang, F., 10.1016/j.ins.2019.10.057, Inform. Sci. 512 (2020), 1592-1605. MR4038642DOI10.1016/j.ins.2019.10.057
  27. Ge, X., Han, Q.-L., Zhong, M., Zhang, X.-M., 10.1016/j.automatica.2019.108557, Automatica 109 (2019), 108557. MR3998774DOI10.1016/j.automatica.2019.108557
  28. Gupta, P., Kumar, P. R., 10.1109/18.825799, IEEE Trans. Inform. Theory 46 (2000), 2, 388-404. MR1748976DOI10.1109/18.825799
  29. Han, F., Dong, H., Wang, Z., Li, G., 10.1002/rnc.4493, Int. J. Robust Nonlinear Control 29 (2019), 8, 2296-2314. MR3940120DOI10.1002/rnc.4493
  30. Han, F., Wei, G., Ding, D., Song, Y., 10.1109/tac.2017.2689722, IEEE Trans. Automat. Control 62 (2017), 9, 4784-4790. MR3691904DOI10.1109/tac.2017.2689722
  31. Heemels, W. P. M. H., Johansson, K. H., Tabuada, P., 10.1109/cdc.2012.6425820, In: Proc. 51st IEEE Conference on Decision and Control, Maui 2012, pp. 3270-3285. MR2952326DOI10.1109/cdc.2012.6425820
  32. Healy, M., Newe, T., Lewis, E., 10.1109/icsens.2008.4716517, In: 2008 IEEE Sensor, Lecce 2008, pp. 621-624. DOI10.1109/icsens.2008.4716517
  33. Hill, J. L., Culler, D. E., 10.1109/mm.2002.1134340, IEEE Micro 22, (2002), 6, 12-24. DOI10.1109/mm.2002.1134340
  34. Hu, J., Wang, Z., Liang, J., Dong, H., 10.1016/j.jfranklin.2014.12.006, J. Franklin Inst. 352 (2015), 3750-3763. MR3385893DOI10.1016/j.jfranklin.2014.12.006
  35. Hu, S., Yue, D., Chen, X., Cheng, Z., Xie, X., 10.1109/tsmc.2019.2896249, IEEE Trans. Systems Man Cybernet.: Systems. DOI10.1109/tsmc.2019.2896249
  36. Jenabzadeh, A., Safarinejadian, B., 10.1016/j.automatica.2017.08.005, Automatica 86 (2017), 53-62. MR3711448DOI10.1016/j.automatica.2017.08.005
  37. Karray, F., Jmal, M. W., Garcia-Ortiz, A., Abid, M., Obeid, A. M., 10.1016/j.comnet.2018.05.010, Comput. Networks 144, (2018), 89-110. DOI10.1016/j.comnet.2018.05.010
  38. Li, J.-Y., Zhang, B., Lu, R., Xu, Y., 10.1109/tsmc.2018.2837047, IEEE Trans. Systems Man Cybernet.: Systems. DOI10.1109/tsmc.2018.2837047
  39. Li, Q., Shen, B., Wang, Z., Shen, W., 10.1016/j.automatica.2019.108681, Automatica 113 (2019), 108681. MR4056010DOI10.1016/j.automatica.2019.108681
  40. Li, Q., Shen, B., Wang, Z., Huang, T., Luo, J., 10.1109/tcyb.2018.2818941, IEEE Trans. Cybernet. 49 (2019), 5, 1979-1986. MR3891660DOI10.1109/tcyb.2018.2818941
  41. Liang, J., Wang, Z., Liu, X., 10.1109/tnn.2011.2105501, IEEE Trans. Neural Networks 22 (2011), 3, 486-496. DOI10.1109/tnn.2011.2105501
  42. Liu, D., Yang, G.-H., 10.1002/rnc.4403, Int. J. Robust Nonlinear Control 29 (2019), 507-518. MR3890676DOI10.1002/rnc.4403
  43. Liu, J., Gu, Y., Cao, J., Fei, S., 10.1016/j.isatra.2018.07.018, ISA Trans. 81 (2018), 63-75. DOI10.1016/j.isatra.2018.07.018
  44. Liu, K., Guo, H., Zhang, Q., Xia, Y., 10.1109/tcyb.2019.2897366, IEEE Trans. Cybernet. DOI10.1109/tcyb.2019.2897366
  45. Liu, Q., Wang, Z., He, X., Zhou, D. H., 10.1109/tii.2015.2444355, IEEE Trans. Industr. Inform. 11 (2015), 6, 1643-1652. MR3671115DOI10.1109/tii.2015.2444355
  46. Liu, S., Liu, P., 10.1109/tii.2017.2766666, IEEE Trans. Industr. Inform. 14 (2018), 5, 1814-1823. DOI10.1109/tii.2017.2766666
  47. Liu, S., Wang, Z., Wei, G., Li, M., 10.1109/tcyb.2018.2885653, IEEE Trans. Cybernetics. DOI10.1109/tcyb.2018.2885653
  48. Liu, Y., Zhao, Y., Wu, F., 10.1049/iet-cta.2015.0654, IET Control Theory Appl. 10 (2016), 4, 431-442. MR3495243DOI10.1049/iet-cta.2015.0654
  49. Ma, L., Wang, Z., Han, Q.-L., Lam, H.-K., 10.1109/jsen.2017.2654325, IEEE Sensors J. 17 (2017), 7, 2279-2288. DOI10.1109/jsen.2017.2654325
  50. Ma, L., Wang, Z., Lam, H.-K., Kyriakoulis, N., 10.1109/tcyb.2016.2582081, IEEE Trans. Cybernet. 47 (2017), 11, 3772-3783. DOI10.1109/tcyb.2016.2582081
  51. Mahmud, R., Toosi, A. N., Ramamohanarao, K., Buyya, R., 10.1109/tii.2019.2952412, IEEE Trans. Industr. Inform. DOI10.1109/tii.2019.2952412
  52. Marin-Perianu, M., Meratnia, N., Havinga, P., et.al., 10.1109/mwc.2007.4407228, IEEE Wireless Commun. 14, (2007), 6, 57-66. DOI10.1109/mwc.2007.4407228
  53. Meral, M., Çelík, D., 10.1016/j.arcontrol.2018.11.003, Ann. Rev. Control 47 (2019), 112-132. MR3973204DOI10.1016/j.arcontrol.2018.11.003
  54. Mihai, V., Dragana, C., Stamatescu, G., Popescu, D., Ichim, L., 10.1109/codit.2018.8394851, In: 5th International Conference on Control, Decision and Information Technologies. Thessaloniki, 2018, pp. 743-747. DOI10.1109/codit.2018.8394851
  55. Millán, P., Orihuela, L., Vivas, C., Rubio, F., 10.1016/j.automatica.2012.06.093, Automatica 48 (2012), 10, 2726-2729. MR2961178DOI10.1016/j.automatica.2012.06.093
  56. Olfati-Saber, R., 10.1109/cdc.2007.4434303, In: Proc. 46th IEEE Conference on Decision and Control, New Orleans 2007, pp. 5492-5498. DOI10.1109/cdc.2007.4434303
  57. Olfati-Saber, R., 10.1109/cdc.2009.5399678, In: Proc. 48h IEEE Conference on Decision and Control, Shanghai 2009, pp. 7036-7042. DOI10.1109/cdc.2009.5399678
  58. Olfati-Saber, R., Jalalkamali, P., 10.1109/tac.2012.2190184, IEEE Trans. Automat. Control 57 (2012), 10, 2609-2614. MR2991662DOI10.1109/tac.2012.2190184
  59. Rafi, A., Rehman, A., Ali, G., Akram, J., 10.1109/icomet.2019.8673423, In: 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur 2019, pp. 1-5. DOI10.1109/icomet.2019.8673423
  60. Rahman, T., Yao, X., Tao, G., Ning, H., Zhou, Z., 10.1109/jsen.2019.2895119, IEEE Sensors J. 19, (2019), 12, 4672-4679. DOI10.1109/jsen.2019.2895119
  61. Satyanarayanan, M., Schuster, R., Ebling, M., Fettweis, G., Flinck, H., Joshi, K., Sabnani, K., 10.1109/mcom.2015.7060484, IEEE Commun. Magazine 53, (2015), 3, 63-70. DOI10.1109/mcom.2015.7060484
  62. Sarkar, S., Wankar, R., Srirama, S., Suryadevara, N. K., 10.1109/jsen.2019.2939182, IEEE Sensors J. 20 (2020), 3, 1564-1572. DOI10.1109/jsen.2019.2939182
  63. Shen, B., Wang, Z., Hung, Y. S., 10.1016/j.automatica.2010.06.025, Automatica 66 (2010), 10, 1682-1688. Zbl1204.93122MR2877323DOI10.1016/j.automatica.2010.06.025
  64. Shen, B., Wang, Z., Liu, X., 10.1109/tcsi.2011.2112594, IEEE Trans. Circuits Systems I: Regular Papers 58 (2011), 9, 2237-2246. MR2868162DOI10.1109/tcsi.2011.2112594
  65. Shen, B., Wang, Z., Qiao, H., 10.1109/tnnls.2016.2516030, IEEE Trans. Neural Networks Learning Systems 28 (2017), 5, 1152-1163. MR3721783DOI10.1109/tnnls.2016.2516030
  66. Song, H., Yu, L., Zhang, W.-A., 10.1049/iet-cta.2013.0432, IET Control Theory Appl. 8 (2014), 3, 202-210. MR3185345DOI10.1049/iet-cta.2013.0432
  67. Souravlias, D., Parsopoulos, K., 10.1007/s13042-014-0308-3, Int. J. Machine Learning Cybernet. 7 (2016), 3, 451-477. DOI10.1007/s13042-014-0308-3
  68. Su, H., Li, Z., Ye, Y., 10.1016/j.isatra.2017.06.019, ISA Trans. 71 (2017), 1, 103-111. MR3468618DOI10.1016/j.isatra.2017.06.019
  69. Su, X., Wu, L., Shi, P., 10.1109/tii.2012.2231085, IEEE Trans. Industr. Inform. 9 (2013), 3, 1739-1750. DOI10.1109/tii.2012.2231085
  70. Sun, Z., Wei, L., Xu, C., Wang, T., Nie, Y., Xing, X., Lu, J., 10.1109/access.2019.2944858, IEEE Access 14, (2019), 7, 144165-144177. DOI10.1109/access.2019.2944858
  71. Tan, Y., Xiong, M., Niu, B., Liu, J., Fei, S., 10.1016/j.neucom.2018.07.022, Neurocomputing 315 (2018), 261-271. DOI10.1016/j.neucom.2018.07.022
  72. Ugrinovskii, V., 10.1016/j.automatica.2010.10.002, Automatica 47 (2011), 1, 1-13. Zbl1209.93152MR2878241DOI10.1016/j.automatica.2010.10.002
  73. Ugrinovskii, V., Fridman, E., 10.1016/j.sysconle.2014.05.001, Systems Control Lett. 69 (2014), 103-110. Zbl1288.93009MR3212828DOI10.1016/j.sysconle.2014.05.001
  74. Ugrinovskii, V., 10.1109/tcns.2019.2924192, IEEE Trans. Control Network Systems 7 (2020), 1, 458-470. DOI10.1109/tcns.2019.2924192
  75. Wan, X., Wang, Z., Han, Q.-L., Wu, M., 10.1109/tcns.2019.2924192, IEEE Trans. Circuits Systems I: Regular Papers 65 (2018), 10, 3481-3491. MR3854691DOI10.1109/tcns.2019.2924192
  76. Wan, X., Wang, Z., Wu, M., Liu, X., 10.1109/tnnls.2018.2839020, IEEE Trans. Neural Networks Learning Systems 30 (2019), 2, 415-426. MR3914858DOI10.1109/tnnls.2018.2839020
  77. Wang, D., Wang, Z., Li, G., Wang, W., 10.1109/jsen.2016.2555761, IEEE Sensors J. 16 (2016), 12, 4940-4948. DOI10.1109/jsen.2016.2555761
  78. Wang, D., Wang, Z., Shen, B., Li, Q., 10.1002/rnc.4479, Int. J. Robust Nonlinear Control 29 (2019), 2096-2111. MR3940107DOI10.1002/rnc.4479
  79. Wang, L., Wang, Z., Han, Q.-L., Wei, G., 10.1109/tcyb.2017.2671032, IEEE Trans. Cybernet. 48 (2018), 3, 1007-1017. MR1988100DOI10.1109/tcyb.2017.2671032
  80. Wang, T., Qiu, J., Fu, S., Ji, W., 10.1109/tie.2016.2622234, IEEE Trans. Industr. Electron. 64 (2017), 6, 5203-5211. DOI10.1109/tie.2016.2622234
  81. Wang, X.-L., Yang, G.-H., 10.1109/tsmc.2018.2882540, IEEE Trans. Systems Man Cybernet.: Systems. DOI10.1109/tsmc.2018.2882540
  82. Wen, C., Wang, Z., Liu, Q., Alsaadi, F. E., 10.1109/tsmc.2016.2629464, IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 6, 930-941. DOI10.1109/tsmc.2016.2629464
  83. Xiao, S., Han, Q.-L., Ge, X., Zhang, Y., 10.1109/tcyb.2019.2900478, IEEE Trans. Cybernet. 50 (2020), 3, 1220-1229. DOI10.1109/tcyb.2019.2900478
  84. Xu, Y., Lu, R., Shi, P., Li, H., Xie, S., 10.1109/tcyb.2016.2635122, IEEE Trans. Cybernet. 48 (2018), 1, 336-345. DOI10.1109/tcyb.2016.2635122
  85. Yan, H., Yang, Q., Zhang, H., Yang, F., Zhan, X., 10.1109/tsmc.2017.2708507, IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 12, 2047-2057. DOI10.1109/tsmc.2017.2708507
  86. Yan, H., Zhang, H., Yang, F., Huang, C., Chen, S., 10.1109/tsmc.2017.2754495, IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 12, 2263-2270. DOI10.1109/tsmc.2017.2754495
  87. Yang, F., Han, Q.-L., Liu, Y., 10.1109/tcyb.2017.2789212, IEEE Trans. Cybernet. 49 (2019), 3, 870-882. DOI10.1109/tcyb.2017.2789212
  88. Yang, F., Xia, N., Han, Q.-L., 10.1109/tii.2016.2607999, IEEE Trans. Industr. Inform. 13 (2017), 1, 322-329. DOI10.1109/tii.2016.2607999
  89. Yang, W., Wang, X. F., Shi, H. B., 10.1080/00207721.2011.565135, Int. J. Systems Sci. 42 (2011), 9, 1521-1529. MR2819529DOI10.1080/00207721.2011.565135
  90. Yin, X., Li, Z., Zhang, L., Han, M., 10.1109/tsmc.2016.2632155, IEEE Trans. Systems Man Cybernet.: Systems 48 (2018), 6, 864-874. DOI10.1109/tsmc.2016.2632155
  91. Yu, H., Zhuang, Y., Wang, W., 10.1016/j.ins.2012.07.059, Inform. Sci. 222 (2013), 424-438. MR2998522DOI10.1016/j.ins.2012.07.059
  92. Yu, W., Deng, Z., Zhou, H., Zeng, X., 10.14736/kyb-2017-5-0747, Kybernetika 53 (2017), 5, 747-764. MR3750101DOI10.14736/kyb-2017-5-0747
  93. Yu, Y., Shen, Y., 10.14736/kyb-2018-4-0699, Kybernetika 54 (2018), 4, 699-717. MR3863251DOI10.14736/kyb-2018-4-0699
  94. Zhang, D., Shi, P., Zhang, W.-A., Yu, L., 10.1109/tcyb.2016.2553043, IEEE Trans. Cybernet. 46 (2017), 7, 1618-1629. MR3537173DOI10.1109/tcyb.2016.2553043
  95. Zhang, D., Yu, L., Zhang, W.-A., 10.1109/jsen.2014.2386348, IEEE Sensors J. 15 (2015), 5, 3026-3036. DOI10.1109/jsen.2014.2386348
  96. Zhang, H., Hong, Q., Yan, H., Yang, F., Guo, G., 10.1109/tii.2016.2569566, IEEE Trans. Industr. Inform. 13 (2017), 1, 312-321. DOI10.1109/tii.2016.2569566
  97. Zhang, H., Wang, Z., Yan, H., Yang, F., Zhou, X., 10.1109/tcyb.2018.2862828, IEEE Trans. Cybernet. 49 (2019), 12, 4296-4307. MR3957647DOI10.1109/tcyb.2018.2862828
  98. Zhang, L., Ning, Z., Wang, Z., 10.1109/tsmc.2015.2435700, IEEE Trans. Systems Man Cybernet.: Systems 46 (2016), 6, 559-572. DOI10.1109/tsmc.2015.2435700
  99. Zhang, P., Wang, J., 10.14736/kyb-2016-4-0589, Kybernetika 52 (2016), 4, 589-606. MR3565771DOI10.14736/kyb-2016-4-0589
  100. Zhang, W.-A., Dong, H., Guo, G., Yu, L., 10.1109/tii.2014.2299897, IEEE Trans. Industr. Inform. 10 (2014), 2, 871-881. DOI10.1109/tii.2014.2299897
  101. Zhang, X.-M., Han, Q.-L., 10.1109/tnnls.2017.2661862, IEEE Trans. Neural Networks Learning Syst. 313 (2018), 29, 1376-1381. MR3867869DOI10.1109/tnnls.2017.2661862
  102. Zhang, X.-M., Han, Q.-L., Ge, X., Ding, D., 10.1016/j.neucom.2018.06.038, Neurocomputing 313 (2018), 392-401. DOI10.1016/j.neucom.2018.06.038
  103. Zhang, X.-M., Han, Q.-L., Ge, X., Ding, D., Ding, L., Yue, D., Peng, C., 10.1109/jas.2019.1911651, IEEE/CAA J. Automat. Sinica 7 (2020), 1, 1-17. MR3841465DOI10.1109/jas.2019.1911651
  104. Zhang, X.-M., Han, Q.-L., Seuret, A., Gouaisbaut, F., He, Y., 10.1049/iet-cta.2018.5188, IET Control Theory Appl. 13 (2019), 1, 1-16. MR3888201DOI10.1049/iet-cta.2018.5188
  105. Zhang, X.-M., Han, Q.-L., Ge, X., 10.1109/jas.2020.1003111, IEEE/CAA J. Automat. Sinica. DOI10.1109/jas.2020.1003111
  106. Zhu, S., Chen, C., Li, W., Yang, B., Guan, X., 10.1109/tsmcb.2012.2236647, IEEE Trans. Cybernet. 43 (2013), 6, 1963-1976. DOI10.1109/tsmcb.2012.2236647
  107. Zhu, Y., Zhang, L., Zheng, W., 10.1109/tie.2015.2499169, IEEE Trans. Industr. Electron. 63 (2016), 3, 1876-1885. DOI10.1109/tie.2015.2499169
  108. Zou, L., Wang, Z., Han, Q.-L., Zhou, D., 10.1109/tac.2017.2713353, IEEE Trans. Automat. Control 62 (2017), 12, 6582-6588. MR3743543DOI10.1109/tac.2017.2713353
  109. Zou, L., Wang, Z., Han, Q.-L., Zhou, D., 10.1109/tac.2017.2713353, IEEE Trans. Automat. Control 64 (2019), 2, 720-727. MR3912120DOI10.1109/tac.2017.2713353
  110. Zou, L., Wang, Z., Han, Q.-L., Zhou, D., 10.1109/tac.2019.2910167, IEEE Trans. Automat. Control 64 (2019), 12, 5191-5198. MR4044317DOI10.1109/tac.2019.2910167
  111. Zou, L., Wang, Z., Han, Q.-L., Zhou, D., 10.1109/tac.2018.2833154, IEEE Trans. Systems Man Cybernet.: Systems. DOI10.1109/tac.2018.2833154

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.