463
Views
1
CrossRef citations to date
0
Altmetric
Regular Articles

Global dissipativity of fuzzy genetic regulatory networks with mixed delays

ORCID Icon, , ORCID Icon &
Pages 2644-2663 | Received 18 Feb 2021, Accepted 17 Mar 2022, Published online: 06 Apr 2022

References

  • Ali, M. S., Gunasekaran, N., Ahn, C. K., & Shi, P. (2016). Sampled-data stabilization for fuzzy genetic regulatory networks with leakage delays. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(1), 271–285. https://doi.org/10.1109/TCBB.2016.2606477
  • Aouiti, C., & Dridi, F. (2020). Delayed fuzzy genetic regulatory networks: novel results. International Journal of Biomathematics, 14(08), 2150028. https://doi.org/10.1142/S1793524521500285
  • Aouiti, C., & Dridi, F. (2021). Study of genetic regulatory networks with Stepanov-like pseudo-weighted almost automorphic coefficients. Neural Computing and Applications, 33(16), 10175–10187. https://doi.org/10.1007/s00521-021-05780-7
  • Aouiti, C., Sakthivel, R., & Touati, F. (2020). Global dissipativity of high-order Hopfield bidirectional associative memory neural networks with mixed delays. Neural Computing and Applications, 32(14), 10183–10197. https://doi.org/10.1007/s00521-019-04552-8
  • Aouiti, C., Sakthivel, R., & Touati, F. (2021). Global dissipativity of fuzzy bidirectional associative memory neural networks with proportional delays. Iranian Journal of Fuzzy Systems, 18(2), 65–80. https://www.sid.ir/en/journal/ViewPaper.aspx?id=867325
  • Cao, J., & Ren, F. (2008). Exponential stability of discrete-time genetic regulatory networks with delays. IEEE Transactions on Neural Networks, 19(3), 520–523. https://doi.org/10.1109/TNN.2007.911748
  • Chen, L., Zhou, Y., & Zhang, X. (2014). Guaranteed cost control for uncertain genetic regulatory networks with interval time-varying delays. Neurocomputing, 131(5439), 105–112. https://doi.org/10.1016/j.neucom.2013.10.035
  • Chen, X., Lin, D., & Lan, W. (2020). Global dissipativity of delayed discrete-time inertial neural networks. Neurocomputing, 390, 131–138. https://doi.org/10.1016/j.neucom.2020.01.073
  • Ding, X., Li, H., Li, X., & Sun, W. (2018). Stability analysis of Boolean networks with stochastic function perturbations. IEEE Access, 7, 1323–1329. https://doi.org/10.1109/ACCESS.2018.2885951
  • Duan, L., Di, F., & Wang, Z. (2020). Existence and global exponential stability of almost periodic solutions of genetic regulatory networks with time-varying delays. Journal of Experimental & Theoretical Artificial Intelligence, 32(3), 453–463. https://doi.org/10.1080/0952813X.2019.1652357
  • Duan, L., Jian, J., & Wang, B. (2020). Global exponential dissipativity of neutral-type BAM inertial neural networks with mixed time-varying delays. Neurocomputing, 378, 399–412. https://doi.org/10.1016/j.neucom.2019.10.082
  • Gao, Y., Xiao, F., Liu, J., & Wang, R. (2018). Distributed soft fault detection for interval type-2 fuzzy-model-based stochastic systems with wireless sensor networks. IEEE Transactions on Industrial Informatics, 15(1), 334–347. https://doi.org/10.1109/TII.2018.2812771
  • He, W., & Cao, J. (2008). Robust stability of genetic regulatory networks with distributed delay. Cognitive Neurodynamics, 2(4), 355–361. https://doi.org/10.1007/s11571-008-9062-0
  • Hu, J., Liang, J., & Cao, J. (2015). Stabilization of genetic regulatory networks with mixed time-delays: an adaptive control approach. IMA Journal of Mathematical Control and Information, 32(2), 343–358. https://doi.org/10.1093/imamci/dnt048
  • Li, P., & Lam, J. (2010). Disturbance analysis of nonlinear differential equation models of genetic SUM regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 8(1), 253–259. https://doi.org/10.1109/TCBB.2010.19
  • Liu, C., Wang, X., & Xue, Y. (2020). Global exponential stability analysis of discrete-time genetic regulatory networks with time-varying discrete delays and unbounded distributed delays. Neurocomputing, 372(1), 100–108. https://doi.org/10.1016/j.neucom.2019.09.047
  • Liu, J., & Jian, J. (2019). Global dissipativity of a class of quaternion-valued BAM neural networks with time delay. Neurocomputing, 349(3), 123–132. https://doi.org/10.1016/j.neucom.2019.03.026
  • Ma, Y., Zhang, Q., & Li, X. (2020). Dissipative control of Markovian jumping genetic regulatory networks with time-varying delays and reaction–diffusion driven by fractional Brownian motion. Differential Equations and Dynamical Systems, 28(4), 841–864. https://doi.org/10.1007/s12591-017-0349-7
  • Manivannan, R., Cao, J., & Chong, K. T. (2020). Generalized dissipativity state estimation for genetic regulatory networks with interval time-delay signals and leakage delays. Communications in Nonlinear Science and Numerical Simulation, 89(7), 105326. https://doi.org/10.1016/j.cnsns.2020.105326
  • Noor, A., Serpedin, E., Nounou, M., & Nounou, H. (2012). Inferring gene regulatory networks via nonlinear state-space models and exploiting sparsity. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(4), 1203–1211. https://doi.org/10.1109/TCBB.2012.32
  • Plahte, E., Gjuvsland, A. B., & Omholt, S. W. (2013). Propagation of genetic variation in gene regulatory networks. Physica D: Nonlinear Phenomena, 256–257, 7–20. https://doi.org/10.1016/j.physd.2013.04.002
  • Poblete, C. M., Parra, F. V., Gomez, J. B., Saldias, M. C., Garrido, S. S., & Vargas, H. M. (2009). Fuzzy logic in genetic regulatory network models. International Journal of Computers Communications & Control, 4(4), 363–373. https://doi.org/10.15837/ijccc.2009.4
  • Qiao, Y., Yan, H., Duan, L., & Miao, J. (2020). Finite-time synchronization of fractional-order gene regulatory networks with time delay. Neural Networks, 126(5), 1–10. https://doi.org/10.1016/j.neunet.2020.02.004
  • Ram, R., Chetty, M., & Dix, T. I. (2006, July). Fuzzy model for gene regulatory network. In 2006 IEEE International Conference on Evolutionary Computation (pp. 1450–1455). IEEE.
  • Raza, K. (2019). Fuzzy logic based approaches for gene regulatory network inference. Artificial Intelligence in Medicine, 97(1), 189–203. https://doi.org/10.1016/j.artmed.2018.12.004
  • Ratnavelu, K., Kalpana, M., & Balasubramaniam, P. (2018). Stability analysis of fuzzy genetic regulatory networks with various time delays. Bulletin of the Malaysian Mathematical Sciences Society, 41(1), 491–505. https://doi.org/10.1007/s40840-016-0427-y
  • Sakthivel, R., Mathiyalagan, K., Lakshmanan, S., & Park, J. H. (2013). Robust state estimation for discrete-time genetic regulatory networks with randomly occurring uncertainties. Nonlinear Dynamics, 74(4), 1297–1315. https://doi.org/10.1007/s11071-013-1041-2
  • Shen, H., Huo, S., Yan, H., Park, J. H., & Sreeram, V. (2019). Distributed dissipative state estimation for Markov jump genetic regulatory networks subject to round-robin scheduling. IEEE Transactions on Neural Networks and Learning Systems, 31(3), 762–771. https://doi.org/10.1109/TNNLS.5962385
  • Su, X., Xia, F., Liu, J., & Wu, L. (2018). Event-triggered fuzzy control of nonlinear systems with its application to inverted pendulum systems. Automatica, 94(6), 236–248. https://doi.org/10.1016/j.automatica.2018.04.025
  • Vasic, B., Ravanmehr, V., & Krishnan, A. R. (2011). An information theoretic approach to constructing robust Boolean gene regulatory networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 9(1), 52–65. https://doi.org/10.1109/TCBB.2011.61
  • Wang, H., Qian, L., & Dougherty, E. (2010). Inference of gene regulatory networks using S-system: a unified approach. IET Systems Biology, 4(2), 145–156. https://doi.org/10.1049/iet-syb.2008.0175
  • Wang, J., Zhang, X. M., & Han, Q. L. (2015). Event-triggered generalized dissipativity filtering for neural networks with time-varying delays. IEEE Transactions on Neural Networks and Learning Systems, 27(1), 77–88. https://doi.org/10.1109/TNNLS.2015.2411734
  • Wang, L., Dong, Y., Xie, D., & Cao, J. (2020). Global dissipativity for stochastic genetic regulatory networks with time-Delays. IEEE Access, 8, 34880–34887. https://doi.org/10.1109/Access.6287639
  • Wang, Z., Liao, X., Mao, J., & Liu, G. (2009). Robust stability of stochastic genetic regulatory networks with discrete and distributed delays. Soft Computing, 13(12), 1199–1208. https://doi.org/10.1007/s00500-009-0417-1
  • Weber, G. W., Defterli, O., Gök, S. Z. A., & Kropat, E. (2011). Modeling, inference and optimization of regulatory networks based on time series data. European Journal of Operational Research, 211(1), 1–14. https://doi.org/10.1016/j.ejor.2010.06.038
  • Xue, Y., Zhang, L., & Zhang, X. (2020). Reachable set estimation for genetic regulatory networks with time-varying delays and bounded disturbances. Neurocomputing, 403(3), 203–210. https://doi.org/10.1016/j.neucom.2020.03.113
  • Yu, T., Liu, J., Zeng, Q., & Wu, L. (2019). Dissipativity-based filtering for switched genetic regulatory networks with stochastic disturbances and time-varying delays. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3), 1082–1092. https://doi.org/10.1109/TCBB.2019.2936351 DOI: 10.1109/TCBB.2019.2936351
  • Yu, T., Liu, J., Zeng, Y., Zhang, X., Zeng, Q., & Wu, L. (2017). Stability analysis of genetic regulatory networks with switching parameters and time delays. IEEE Transactions on Neural Networks and Learning Systems, 29(7), 3047–3058. https://doi.org/10.1109/ACCESS.2020.2974616
  • Zhang, L., Zhang, X., Xue, Y., & Zhang, X. (2020). New method to global exponential stability analysis for switched genetic regulatory networks with mixed delays. IEEE Transactions on NanoBioscience, 19(2), 308–314. https://doi.org/10.1109/TNB.7728
  • Zhang, W., Fang, J. A., & Tang, Y. (2011). New robust stability analysis for genetic regulatory networks with random discrete delays and distributed delays. Neurocomputing, 74(14–15), 2344–2360. https://doi.org/10.1016/j.neucom.2011.03.011
  • Zhang, X., Fan, X., & Wu, L. (2017). Reduced-and full-order observers for delayed genetic regulatory networks. IEEE Transactions on Cybernetics, 48(7), 1989–2000. https://doi.org/10.1109/TCYB.2017.2726015
  • Zhang, X., Wu, L., & Cui, S. (2014). An improved integral inequality to stability analysis of genetic regulatory networks with interval time-varying delays. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(2), 398–409. https://doi.org/10.1109/TCBB.2014.2351815
  • Zhang, X., Wu, L., & Zou, J. (2015). Globally asymptotic stability analysis for genetic regulatory networks with mixed delays: an M-matrix-based approach. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 13(1), 135–147. https://doi.org/10.1109/TCBB.2015.2424432
  • Zhang, X. M., & Han, Q. L. (2015). A decentralized event-triggered dissipative control scheme for systems with multiple sensors to sample the system outputs. IEEE Transactions on Cybernetics, 46(12), 2745–2757. https://doi.org/10.1109/TCYB.2015.2487420
  • Zhao, Y., Wang, J., Yan, F., & Shen, Y. (2019). Adaptive sliding mode fault-tolerant control for type-2 fuzzy systems with distributed delays. Information Sciences, 473(6), 227–238. https://doi.org/10.1016/j.ins.2018.09.002
  • Zhou, L. (2021). Global exponential dissipativity of impulsive recurrent neural networks with multi-proportional delays. Neural Processing Letters, 53(2), 1435–1452. https://doi.org/10.1007/s11063-021-10451-8

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.