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Original Articles

Extended dissipativity of generalised neural networks including time delays

, , &
Pages 2311-2320 | Received 06 Nov 2016, Accepted 02 Apr 2017, Published online: 02 May 2017

References

  • El Haoussi, F., Tissir, E. H., Tadeo, F., & Hmamed, A. (2011). Delay-dependent stabilisation of systems with time-delayed state and control: Application to a quadruple-tank process. International Journal of Systems Science, 42(1), 41–49.
  • Fan, K., Taussky, O., & Todd, J. (1955). Discrete analogs of inequalities of Wirtinger. Monatshefte für Mathematik, (59), 73–90. doi:10.1109/TFUZZ.2016.2566800
  • Feng, Z., & Lam, J. (2011). Stability and dissipativity analysis of distributed delay cellular neural networks. IEEE Transactions on Neural Networks, 22(6), 976–981.
  • Feng, Z., Lam, J., & Gao, H. (2011). α-dissipativity analysis of singular time-delay systems. Automatica, 47(11), 2548–2552.
  • Feng, Z., & Zheng, W. X. (2015). On extended dissipativity of discrete-time neural networks with time delay. IEEE Transactions on Neural Networks and Learning Systems, 26(12), 3293–3300.
  • Haykin, S. (1994). Neural networks: A comprehensive foundation. New York, NY: Prentice Hall.
  • Hien, L., & Trinh, H. (2016). Exponential stability of time-delay systems via new weighted integral inequalities. Applied Mathematics and Computation, 275, 335–344.
  • Lee, T. H., Park, M. -J., Park, J. H., Kwon, O. -M., & Lee, S. -M. (2014). Extended dissipative analysis for neural networks with time-varying delays. IEEE Transactions on Neural Networks and Learning Systems, 25(10), 1936–1941.
  • Li, L., Ding, S. X., Qiu, J., & Yang, Y. (2017). Real-time fault detection approach for nonlinear systems and its asynchronous TS fuzzy observer-based implementation. IEEE transactions on cybernetics, 47(2), 283–294.
  • Li, L., Ding, S. X., Qiu, J., Yang, Y., & Zhang, Y. (2016a). Weighted fuzzy observer-based fault detection approach for discrete-time nonlinear systems via piecewise-fuzzy Lyapunov functions. IEEE Transactions on Fuzzy Systems, 24(6), 1320–1333.
  • Li, Y., Zhong, S., Cheng, J., Shi, K., & Ren, J. (2016b). New passivity criteria for uncertain neural networks with time-varying delay. Neurocomputing, 171, 1003–1012.
  • Liu, Y., Lee, S., Kwon, O., & Park, J. H. (2015). New approach to stability criteria for generalized neural networks with interval time-varying delays. Neurocomputing, 149, 1544–1551.
  • Liu, Y., Wang, Z., & Liu, X. (2006). Global exponential stability of generalized recurrent neural networks with discrete and distributed delays. Neural Networks, 19(5), 667–675.
  • Qiu, J., Ding, S. X., Gao, H., & Yin, S. (2016a). Fuzzy-model-based reliable static output feedback H∞ control of nonlinear hyperbolic PDE systems. IEEE Transactions on Fuzzy Systems, 24(2), 388–400.
  • Qiu, J., Gao, H., & Ding, S. X. (2016b). Recent advances on fuzzy-model-based nonlinear networked control systems: A survey. IEEE Transactions on Industrial Electronics, 63(2), 1207–1217.
  • Saravanakumar, R., Ali, M. S., Ahn, C. K., Karimi, H., & Shi, P. (2016a). 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.
  • Saravanakumar, R., Ali, M. S., Cao, J., & Huang, H. (2016b). H∞ state estimation of generalised neural networks with interval time-varying delays. International Journal of Systems Science, 47(16), 3888–3899.
  • Saravanakumar, R., Ali, M. S., & Karimi, H. R. (2017). Robust H∞ control of uncertain stochastic Markovian jump systems with mixed time-varying delays. International Journal of Systems Science. 48(4), 862–872. doi:10.1080/00207721.2016.1218092.
  • Seuret, A., Gouaisbaut, F., & Fridman, E. (2013). Stability of systems with fast-varying delay using improved Wirtinger's inequality. 49(9),2860–2866.
  • Shen, H., Park, J., Zhang, L., & Wu, Z.-G. (2014). Robust extended dissipative control for sampled-data Markov jump systems. International Journal of Control, 87(8), 1549–1564.
  • Shen, H., Zhu, Y., Zhang, L., & Park, J. (2017). Extended dissipative state estimation for markov jump neural networks with unreliable links. IEEE Transactions on Neural Networks and Learning Systems. 28(2), 346–358. doi:10.1109/TNNLS.2015.2511196.
  • Thuan, M., Trinh, H., & Hien, L. (2016). New inequality-based approach to passivity analysis of neural networks with interval time-varying delay. Neurocomputing, 194, 301–307.
  • Wang, T., Gao, H., & Qiu, J. (2016a). A combined fault-tolerant and predictive control for network-based industrial processes. IEEE Transactions on Industrial Electronics, 63(4), 2529–2536.
  • Wang, T., Qiu, J., & Gao, H. (2016b). Adaptive neural control of stochastic nonlinear time-delay systems with multiple constraints. IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi: 10.1109/TSMC.2016.2562511
  • Wang, T., Qiu, J., Yin, S., Gao, H., Fan, J., & Chai, T. (2016c). Performance-based adaptive fuzzy tracking control for networked industrial processes. IEEE Transactions on Cybernetics, 46(8), 1760–1770.
  • Wang, X., She, K., Zhong, S., & Cheng, J. (2016d). On extended dissipativity analysis for neural networks with time-varying delay and general activation functions. Advances in Difference Equations, 2016(79), 1–16.
  • Wei, H., Li, R., Chen, C., & Tu, Z. (2016a). Extended dissipative analysis for memristive neural networks with two additive time-varying delay components. Neurocomputing, 216, 429–438.
  • Wei, Y., Qiu, J., & Fu, S. (2015). Mode-dependent nonrational output feedback control for continuous-time semi-Markovian jump systems with time-varying delay. Nonlinear Analysis: Hybrid Systems, 16, 52–71.
  • Wei, Y., Qiu, J., Karimi, H. R., & Wang, M. (2014a). Filtering design for two-dimensional Markovian jump systems with state-delays and deficient mode information. Information Sciences, 269, 316–331.
  • Wei, Y., Qiu, J., Karimi, H. R., & Wang, M. (2014b). New results on H∞ dynamic output feedback control for Markovian jump systems with time-varying delay and defective mode information. Optimal Control Applications and Methods, 35(6), 656–675.
  • Wei, Y., Qiu, J., Shi, P., & Lam, H.-K. (2016b). A new design of H∞ piecewise filtering for discrete-time nonlinear time-varying delay systems via TS fuzzy affine models. IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi:10.1109/TSMC.2016.2598785.
  • Wei, Y., Wang, M., & Qiu, J. (2013). New approach to delay-dependent H∞ filtering for discrete-time Markovian jump systems with time-varying delay and incomplete transition descriptions. IET Control Theory & Applications, 7(5), 684–696.
  • Willems, J. (1971). The analysis of feedback systems. Cambridge: The MIT Press.
  • Wu, Z., Lam, J., Su, H., & Chu, J. (2012a). Stability and dissipativity analysis of static neural networks with time delay. IEEE Transactions on Neural Networks and Learning Systems, 23(2), 199–210.
  • Wu, Z., Park, J., Su, H., & Chu, J. (2012b). Robust dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties. Nonlinear Dynamics, 69, 1323–1332.
  • Xiao, J., Zhong, S., & Li, Y. (2016). Improved passivity criteria for memristive neural networks with interval multiple time-varying delays. Neurocomputing, 171, 1414–1430.
  • Yang, F., Dong, H., Wang, Z., Ren, W., & Alsaadi, F. E. (2016). A new approach to non-fragile state estimation for continuous neural networks with time-delays. Neurocomputing, 197, 205–211.
  • Zeng, H., He, Y., Shi, P., Wu, M., & Xiao, S. (2015a). Dissipativity analysis of neural networks with time-varying delays. Neurocomputing, 168, 741–746.
  • Zeng, H., Park, J., & Shen, H. (2015b). Robust passivity analysis of neural networks with discrete and distributed delays. Neurocomputing, 149, 1092–1097.
  • Zeng, H., Park, J., & Xia, J. (2015c). Further results on dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties. Nonlinear Dynamics, 79, 83–91.
  • Zeng, H., Park, J., Zhang, C., & Wang, W. (2015d). Stability and dissipativity analysis of static neural networks with interval time-varying delay. Journal of the Franklin Institute, 352, 1284–1295.
  • Zeng, H. B., He, Y., Wu, M., & Xiao, S. P. (2015e). Stability analysis of generalized neural networks with time-varying delays via a new integral inequality. Neurocomputing, 161, 148–154.
  • Zhang, B., Xu, S., & Lam, J. (2014a). Relaxed passivity conditions for neural networks with time-varying delays. Neurocomputing, 142, 299–306.
  • Zhang, B., Zheng, W. X., & Xu, S. (2013). Filtering of markovian jump delay systems based on a new performance index. IEEE Transactions on Circuits and Systems I: Regular, 60(5), 1250–1263.
  • Zhang, C. K., He, Y., Jiang, L., Wu, Q. H., & Wu, M. (2014b). Delay-dependent stability criteria for generalized neural networks with two delay components. IEEE Transactions on Neural Networks and Learning Systems, 25(7), 1263–1276.
  • Zhang, X., & Han, Q. (2011). Global asymptotic stability for a class of generalized neural networks with interval time-varying delays. IEEE Transactions on Neural Networks, 22(8), 1180–1192.

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