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Original Articles

Event-triggered state estimation for Markovian jumping impulsive neural networks with interval time-varying delays

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Pages 270-290 | Received 16 Nov 2016, Accepted 30 Jun 2017, Published online: 27 Jul 2017

References

  • Ahn, C. K. (2011). H ∞ State estimation for Takagi-Sugeno fuzzy delayed hopfield neural networks. International Journal of Computational Intelligence Systems, 4 (5), 855–862.
  • Ahn, C. K. (2012). Switched exponential state estimation of neural networks based on passivity theory. Nonlinear Dynamics, 67 (1), 573–586.
  • Arik, S. (2004). An analysis of exponential stability of delayed neural networks with time varying delays. Neural Networks, 17 (7), 1027–1031.
  • Balasubramaniam, P. , & Nagamani, G. (2010). Passivity analysis of neural networks with Markovian jumping parameters and interval time-varying delays. Nonlinear Analysis: Hybrid Systems, 4 (4), 853–864.
  • Balasubramaniam, P. , Lakshmanan, S. , & Jeeva Sathya Theesar, S. (2011). State estimation for Markovian jumping recurrent neural networks with interval time-varying delays. Nonlinear Dynamics, 60 (4), 661–675.
  • Balasubramaniam, P. , Krishnasamy, R. , & Rakkiyappan, R. (2012). Delay-dependent stability of neutral systems with time-varying delays using delay decomposition approach. Applied Mathematical Modelling, 36 (5), 2253–2261.
  • Chong Wei, P. , Liang Wang, J. , Li Huang, Y. , Bei Xu, B. , & Yan Ren, S. (2015). Passivity analysis of impulsive coupled reaction-diffusion neural networks with and without time-varying delay. Neurocomputing, 168 , 13–22.
  • Cichocki, A. , & Unbehauen, R. (1993). Neural networks for optimization and signal processing. Chichester: Wiley.
  • Dong, H. , Bu, X. , Hou, N. , Liu, Y. , Alsaadi, F. E. , & Hayat, T. (2017). Event-triggered distributed state estimation for a class of time-varying systems over sensor networks with redundant channels. Information Fusion, 36 , 243–250.
  • Gao, L. , Wang, D. , & Wang, G. (2015). Further results on exponential stability for impulsive switched nonlinear time-delay systems with delayed impulse effects. Applied Mathematics and Computation, 268 , 186–200.
  • He, Y. , Liu, G. P. , Rees, D. , & Wu, M. (2007). Stability analysis for neural networks with time-varying interval delay. IEEE Transactions on Neural Networks, 18 (6), 1850–1854.
  • Hou, N. , Dong, H. , Wang, Z. , Ren, W. , & Alsaadi, F. (2016). Non-fragile state estimation for discrete Markovian jumping neural networks. Neurocomputing, 179 , 238–245.
  • Hu, S. , & Yue, D. (2012). Event-triggered control design of linear networked systems with quantizations. ISA Transactions, 51 (1), 153–162.
  • Huang, J. , Shi, D. , & Chen, T. (2017). Energy-based event-triggered state estimation for hidden Markov models. Automatica, 79 , 256–264.
  • Kovacic, M. (1991). Markovian neural networks. Biological Cybernetics, 64 (4), 337–342.
  • Kwon, O. M. , Park, J. H. , & Lee, S. M. (2008). On robust stability for uncertain neural networks with interval time-varying delays. IET Control Theory & Applications, 2 , 625–634.
  • Lee, T. H. , Park, J. H. , Kwon, O. M. , & Lee, S. M. (2013). Stochastic sampled-data control for state estimation of time-varying delayed neural networks. Neural Networks, 46 , 99–108.
  • Li, C. , Li, Y. , & Ye, Y. (2010). Exponential stability of fuzzy Cohen-Grossberg neural networks with time delays and impulsive effects. Communications in Nonlinear Science and Numerical Simulation, 15 (11), 3599–3606.
  • Li, T. , Zheng, W. X. , & Lin, C. (2011). Delay-slope-dependent stability results of recurrent neural networks. IEEE Transactions on Neural Networks, 22 (12), 2138–2143.
  • Li, X. , & Song S., (2013). Impulsive control for existence, uniqueness, and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays. IEEE Transactions on Neural Networks and Learning Systems, 24 (6), 868–877.
  • Li, X. , O'Regan, D. , & Akca, H. (2015). Global exponential stabilization of impulsive neural networks with unbounded continuously distributed delays. IMA Journal of Applied Mathematics, 80 , 85–99.
  • Li, X. , Bohner, M. , & Chuan-Kui, W. (2015). Impulsive differential equations: Periodic solutions and applications. Automatica, 52 , 173–178.
  • Li, S. , Xiang, Z. , Lin, H. , & Karimi, H. R. (2016). State estimation on positive Markovian jump systems with time-varying delay and uncertain transition probabilities. Information Sciences, 369 , 251–266.
  • Li, H. , Zuo, Z. , & Wang, Y. (2016). Event triggered control for Markovian jump systems with partially unknown transition probabilities and actuator saturation. Journal of the Franklin Institute, 353 (8), 1848–1861.
  • Li, Q. , Shen, B. , Liu, Y. , & Alsaadi, F. E. (2016). Event-triggered H ∞ state estimation for discrete-time stochastic genetic regulatory networks with Markovian jumping parameters and time-varying delays. Neurocomputing, 174 , 912–920.
  • Li, Q. , Shen, B. , Liu, Y. , & Huang, T. (2016). Event-triggered H ∞ state estimation for discrete-time neural networks with mixed time delays and sensor saturations. Neural Computing and Applications . doi:10.1007/s00521-016-2271-2
  • Lu, R. , Wu, F. , & Xue, A. (2014). Networked control with reset quantized state based on Bernoulli processing. IEEE Transactions on Industrial Electronics, 61 (9), 4838–4846.
  • Lu, R. , Xu, Y. , & Zhang, R. (2016). A new design of model predictive tracking control for networked control system under random packet loss and uncertainties. IEEE Transactions on Industrial Electronics, 63 (11), 6999–7007.
  • Park, P. G. , Ko, J. W. , & Jeong, C. K. (2011). Reciprocally convex approach to stability of systems with time-varying delays. Automatica, 47 (1), 235–238.
  • Park, J. H. , Mathiyalagan, K. , & Sakthivel, R. (2016). Fault estimation for discrete-time switched nonlinear systems with discrete and distributed delays. International Journal of Robust and Nonlinear Control, 26 (17), 3755–3771.
  • Qiu, J. , Yang, H. , Zhang, J. , & Gao, Z. (2009). New robust stability criteria for uncertain neural networks with interval time-varying delays. Chaos, Solitons & Fractals, 39 (2), 579–585.
  • Rakkiyappan, R. , Sakthivel, N. , Park, J. H. , & Kwon, O. M. (2013). Sampled-data state estimation for Markovian jumping fuzzy cellular neural networks with mode-dependent probabilistic time-varying delays. Applied Mathematics and Computation, 221 , 741–769.
  • Sakthivel, R. , Samidurai, R. , & Anthoni, M. (2010). Asymptotic stability of stochastic delayed Recurrent neural networks with impulsive effects. Journal of Optimization Theory and Applications, 147 (3), 583–596.
  • Saravanakumar, R. , Syed Ali, M. , Ahn, C. K. , Karimi, H. R. , & Shi, P. (2016). Stability of Markovian jump generalized neural networks with interval time-varying delays. IEEE Transactions on Neural Networks and Learning Systems . doi:10.1109/TNNLS.2016.2552491
  • Senthilraj, S. , Raja, R. , Zhu, Q. , Samidurai, R. , & Zhou, H. (2016). Delay-dependent asymptotic stability criteria for genetic regulatory networks with impulsive perturbations. Neurocomputing, 214 , 981–990.
  • Seuret, A. , & Gouaisbaut, F. (2013). Wirtinger-based integral inequality: Application to time-delay systems. Automatica, 49 (9), 2860–2866.
  • Shao, L. , Huang, H. , Zhao, H. , & Huang, T. (2015). Filter design of delayed static neural networks with Markovian jumping parameters. Neurocomputing, 153 , 126–132.
  • Sheng, L. , Wang, Z. , Zou, L. , & Alsaadi, F. E. (2016). Event-based H ∞ state estimation for time-varying stochastic dynamical networks with state and disturbance-dependent noises. IEEE Transactions on Neural Networks and Learning Systems, doi:10.1007/s00521-016-2271-
  • Shen, B. , Wang, Z. , & Qiao, H. (2017). Event-triggered state estimation for discrete-time multidelayed neural networks with stochastic parameters and incomplete measurements. IEEE Transactions on Neural Networks and Learning Systems , 28(5), 1152–1163.
  • Shi, P. , Zhang, Y. , & Agarwal, R. K. (2016). Stochastic finite-time state estimation for discrete time-delay neural networks with Markovian jumps. Neurocomputing, 151 , 168–174.
  • Sun, Y. , Fu, M. , Wang, B. , Zhang, H. , & Marelli, D. (2016). Dynamic state estimation for power networks using distributed MAP technique. Automatica, 73 , 27–37.
  • Syed Ali, M. (2015). Stability of Markovian jumping recurrent neural networks with discrete and distributed time-varying delays. Neurocomputing, 149 , 1280–1285.
  • Syed Ali, M. , Saravanakumar, R. , & Arik, S. (2015). Delay-dependent stability criteria of uncertain Markovian jump neural networks with discrete interval and distributed time-varying delays. Neurocomputing, 158 , 167–173.
  • Lee, T. H. , Lakshmanan, S. , Park, J. H. , & Balasubramaniam, P. (2013). State estimation for genetic regulatory networks with mode-dependent leakage delays, time-varying delays, and markovian jumping parameters. IEEE Transactions on NanoBioscience, 12 (4), 363–375.
  • Tan, Y. , Du, D. , & Qi, Q. (2016). State estimation for Markovian jump systems with an event-triggered communication scheme. Circuits, Systems, and Signal Processing, 36 (1), 2–24.
  • Wang, H. , Shi, P. , Lim, C. , & Xue, Q. (2015). Event-triggered control for networked Markovian jump systems. International Journal of Robust and Nonlinear Control, 25 (17), 3422–3438.
  • Wang, J. , Zhang, X. M. , & Han, Q. L. (2016). Event-triggered generalized dissipativity filtering for neural networks with time-varying delays. IEEE Transactions on Neural Networks and Learning Systems, 27 (1), 77–88.
  • Wang, L. , Wang, Z. , Wei, G. , & Alsaadi, F. E. (2017). Finite-time state estimation for recurrent delayed neural networks with component-based event-triggering protocol. IEEE Transactions on Neural Networks and Learning Systems . doi:10.1109/TNNLS.2016.2635080
  • Wu, H. , Wang, W. , Ye, H. , & Wang, Z. (2013). State estimation for Markovian jump linear systems with bounded disturbances. Automatica, 49 , 3292–3303.
  • Wu, Y. , Lu, R. , Shi, P. , Su, H. , & Wu, Z.-G. (2017). Adaptive output synchronization of heterogeneous network with an uncertain leader. Automatica, 76 , 183–192.
  • Wu, Y. , Meng, X. , Xie, L. , Lu, R. , Su, H. , & Zheng-Guang, W. (2017). An input-based triggering approach to leader-following problems. Automatica, 75 , 221–228.
  • Xie, J. , & Kao, Y. (2015). Stability of Markovian jump neural networks with mode-dependent delays and generally incomplete transition probability. Neural Computing and Applications, 26 (7), 1537–1553.
  • Xu, Y. , Lu, R. , Peng, H. , Xie, K. , & Xue, A. (2017). Asynchronous dissipative state estimation for stochastic complex networks with quantized jumping coupling and uncertain measurements. IEEE Transactions on Neural Networks and Learning Systems, 28 (2), 268–277.
  • Xu, Y. , Lu, R. , Shi, P. , Li, H. , & Xie, S. (2016). Finite-time distributed state estimation over sensor networks with Round-Robin protocol and fading channels. IEEE Transactions on Cybernetics, 99 , 1–10.
  • Xue, A. , Wang, H. , & Lu, R. (2016). Event-based H ∞ control for discrete Markov jump systems. Neurocomputing, 190 , 165–171.
  • Zhang, W. , Tang, Y. , Miao, Q. , & Du, W. (2013). Exponential synchronization of coupled switched neural networks with mode-dependent impulsive effects. IEEE Transactions on Neural Networks and Learning Systems, 24 (8), 1316–1326.
  • Zhang, X. M. , & Han, Q. L. (2014). Event-triggered dynamic output feedback control for networked control systems. IET Control Theory & Applications, 8 (4), 226–234.
  • Zhang, W. , Wang, Z. , Liu, Y. , Ding, D. , & Alsaadi, F. E. (2017). Event-based state estimation for a class of complex networks with time-varying delays: A comparison principle approach. Physics Letters A, 381 (1), 10–18.
  • Zhou, X. , Tian, J. , Ma, H. , & Zhong, S. (2014). Improved delay-dependent stability criteria for recurrent neural networks with time-varying delays. Neurocomputing, 129 , 401–408.
  • Zhu, Q. , & Cao, J. (2010). Stability analysis for stochastic neural networks of neutral type with both Markovian jump parameters and mixed time delays. Neurocomputing, 73 , 2671–2680.

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