[1] B. Wang, J. Y. Zhai, S. M. Fei. Output feedback tracking control for a class of switched nonlinear systems with time-varying delay. International Journal of Automation and Computing, vol. 11, no. 6, pp. 605-612, 2014. doi:  10.1007/s11633-014-0848-8
[2] T. Wang, M. X. Xue, C. Zhang, S. M. Fei. Improved stability criteria on discrete-time systems with time-varying and distributed delays. International Journal of Automation and Computing, vol. 10, no. 3, pp. 260-266, 2013. doi:  10.1007/s11633-013-0719-8
[3] L. Chen, H. Y. Zhao. Stability analysis of stochastic fuzzy cellular neural networks with delays. Neurocomputing, vol. 72, no. 1-3, pp. 436-444, 2008. doi:  10.1016/j.neucom.2007.12.005
[4] Z. G. Zeng, J. Wang, X. X. Liao. Global exponential stability of a general class of recurrent neural networks with timevarying delays. IEEE Transactions on Circuits and Systems Ⅰ: Fundamental Theory and Applications, vol. 50, no. 10, pp. 1353-1358, 2003. doi:  10.1109/TCSI.2003.817760
[5] H. J. Jiang, J. D. Cao. Global exponential stability of periodic neural networks with time-varying delays. Neurocomputing, vol. 70, no. 1-3, pp. 343-350, 2006. doi:  10.1016/j.neucom.2006.01.021
[6] Y. G. Chen, W. L. Li, W. P. Bi. Improved results on passivity analysis of uncertain neural networks with time-varying discrete and distributed delays. Neural Processing Letters, vol. 30, no. 2, pp. 155-169, 2009. doi:  10.1007/s11063-009-9116-2
[7] H. Huang, G. Feng, J. D. Cao. Robust state estimation for uncertain neural networks with time-varying delay. IEEE Transactions on Neural Networks, vol. 19, no. 8, pp. 1329- 1339, 2008. doi:  10.1109/TNN.2008.2000206
[8] H. G. Zhang, Z. W. Liu, G. B. Huang, Z. S. Wang. Novel weighting-delay-based stability criteria for recurrent neural networks with time-varying delay. IEEE Transactions on Neural Networks, vol. 21, no. 1, pp. 91-106, 2010. doi:  10.1109/TNN.2009.2034742
[9] O. M. Kwon, S. M. Lee, J. H. Park, E. J. Cha. New approaches on stability criteria for neural networks with interval time-varying delays. Applied Mathematics and Computation, vol. 218, no. 19, pp. 9953-9964, 2012. doi:  10.1016/j.amc.2012.03.082
[10] S. M. Lee, O. M. Kwon, J. H. Park. A novel delaydependent criterion for delayed neural networks of neutral type. Physics Letters A, vol. 374, no. 17-18, pp. 1843-1848, 2010. doi:  10.1016/j.physleta.2010.02.043
[11] T. Wang, M. X. Xue, S. M. Fei, T. Li. Triple Lyapunov functional technique on delay-dependent stability for discrete-time dynamical networks. Neurocomputing, vol. 122, pp. 221-228, 2013. doi:  10.1016/j.neucom.2013.05.039
[12] L. Jarina Banu, P. Balasubramaniam. Synchronisation of discrete-time complex networks with randomly occurring uncertainties, nonlinearities and time-delays. International Journal of Systems Science, vol. 45, no. 7, pp. 1427-1450, 2014. doi:  10.1080/00207721.2013.844287
[13] K. Gopalsamy. Leakage delays in BAM. Journal of Mathematical Analysis and Applications, vol. 325, no. 2, pp. 1117- 1132, 2007. doi:  10.1016/j.jmaa.2006.02.039
[14] X. D. Li, X. L. Fu, P. Balasubramaniam, R. Rakkiyappan. Existence, uniqueness and stability analysis of recurrent neural networks with time delay in the leakage term under impulsive perturbations. Nonlinear Analysis: Real World Applications, vol. 11, no. 5, pp. 4092-4108, 2010. doi:  10.1016/j.nonrwa.2010.03.014
[15] Y. Wang, C. D. Zheng, E. M. Feng. Stability analysis of mixed recurrent neural networks with time delay in the leakage term under impulsive perturbations. Neurocomputing, vol. 119, pp. 454-461, 2013. doi:  10.1016/j.neucom.2013.03.012
[16] R. Sakthivel, P. Vadivel, K. Mathiyalagan, A. Arunkumar, M. Sivachitra. Design of state estimator for bidirectional associative memory neural networks with leakage delays. Information Sciences, vol. 296, pp. 263-274, 2015. doi:  10.1016/j.ins.2014.10.063
[17] S. J. Long, Q. K. Song, X. H. Wang, D. S. Li. Stability analysis of fuzzy cellular neural networks with time delay in the leakage term and impulsive perturbations. Journal of the Franklin Institute, vol. 349, no. 7, pp. 2461-2479, 2012. doi:  10.1016/j.jfranklin.2012.05.009
[18] L. Jarina Banu, P. Balasubramaniam, K. Ratnavelu. Robust stability analysis for discrete-time uncertain neural networks with leakage time-varying delay. Neurocomputing, vol. 151, Part 2, pp. 808-816, 2015. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=f2b1ade697649c4cc8361f3477aae8ea
[19] P. Balasubramaniam, V. Vembarasan, R. Rakkiyappan. Global robust asymptotic stability analysis of uncertain switched Hopfleld neural networks with time delay in the leakage term. Neural Computing and Applications, vol. 21, no. 7, pp. 1593-1616, 2012. doi:  10.1007/s00521-011-0639-x
[20] R. Rakkiyappan, A. Chandrasekar, S. Lakshmanan, J. H. Park, H. Y. Jung. Efiects of leakage time-varying delays in Markovian jump neural networks with impulse control. Neurocomputing, vol. 121, pp. 365-378, 2013. doi:  10.1016/j.neucom.2013.05.018
[21] P. Balasubramaniam, V. Vembarasan, R. Rakkiyappan. Leakage delays in T-S fuzzy cellular neural networks. Neural Processing Letters, vol. 33, no. 2, pp. 111-136, 2011. doi:  10.1007/s11063-010-9168-3
[22] X. D. Li, J. D. Cao. Delay-dependent stability of neural networks of neutral type with time delay in the leakage term. Nonlinearity, vol. 23, no. 7, pp. 1709-1726, 2010. doi:  10.1088/0951-7715/23/7/010
[23] P. Balasubramaniam, M. Kalpana, R. Rakkiyappan. Global asymptotic stability of BAM fuzzy cellular neural networks with time delay in the leakage term, discrete and unbounded distributed delays. Mathematical and Computer Modelling, vol. 53, no. 5-6, pp. 839-853, 2011. doi:  10.1016/j.mcm.2010.10.021
[24] M. J. Park, O. M. Kwon, J. H. Park, S. M. Lee, E. J. Cha. Synchronization criteria for coupled stochastic neural networks with time-varying delays and leakage delay. Journal of the Franklin Institute, vol. 349, no. 5, pp. 1699-1720, 2012. doi:  10.1016/j.jfranklin.2012.02.002
[25] Z. J. Zhao, Q. K. Song, S. R. He. Passivity analysis of stochastic neural networks with time-varying delays and leakage delay. Neurocomputing, vol. 125, pp. 22-27, 2014. doi:  10.1016/j.neucom.2012.08.049
[26] X. Y. Lou, B. T. Cui. Stochastic exponential stability for Markovian jumping BAM neural networks with timevarying delays. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 3, pp. 713- 719, 2007. doi:  10.1109/TSMCB.2006.887426
[27] C. D. Zheng, Y. Wang, Z. S. Wang. Stability analysis of stochastic fuzzy Markovian jumping neural networks with leakage delay under impulsive perturbations. Journal of the Franklin Institute, vol. 351, no. 3, pp. 1728-1755, 2014. doi:  10.1016/j.jfranklin.2013.12.013
[28] Q. X. Zhu, R. Rakkiyappan, A. Chandrasekar. Stochastic stability of Markovian jump BAM neural networks with leakage delays and impulse control. Neurocomputing, vol. 136, pp. 136-151, 2014. doi:  10.1016/j.neucom.2014.01.018
[29] Q. X. Zhu, J. D. Cao. Robust exponential stability of Markovian jump impulsive stochastic Cohen-Grossberg neural networks with mixed time delays. IEEE Transactions on Neural Networks, vol. 21, no. 8, pp. 1314-1325, 2010. doi:  10.1109/TNN.2010.2054108
[30] Y. R. Liu, Z. D. Wang, X. H. Liu. An LMI approach to stability analysis of stochastic high-order Markovian jumping neural networks with mixed time delays. Nonlinear Analysis: Hybrid Systems, vol. 2, no. 1, pp. 110-120, 2008. doi:  10.1016-j.nahs.2007.06.001/
[31] Z. D. Wang, D. W. C. Ho, X. H. Liu. State estimation for delayed neural networks. IEEE Transactions on Neural Networks, vol. 16, no. 1, pp. 279-284, 2005. doi:  10.1109/TNN.2004.841813
[32] Y. He, Q. G. Wang, M. Wu, C. Lin. Delay-dependent state estimation for delayed neural networks. IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 1077-1081, 2006. doi:  10.1109/TNN.2006.875969
[33] H. Huang, G. Feng, J. D. Cao. Robust state estimation for uncertain neural networks with time-varying delay. IEEE Transactions on Neural Networks, vol. 19, no. 8, pp. 1329- 1339, 2008. doi:  10.1109/TNN.2008.2000206
[34] C. Y. Lu. A delay-range-dependent approach to design state estimator for discrete-time recurrent neural networks with interval time-varying delay. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, vol. 55, no. 11, pp. 1163- 1167, 2008. doi:  10.1109/TCSII.2008.2001988
[35] S. S. Mou, H. J. Gao, W. Y. Qiang, Z. Y. Fei. State estimation for discrete-time neural networks with time-varying delays. Neurocomputing, vol. 72, no. 1-3, pp. 643-647, 2008. doi:  10.1016/j.neucom.2008.06.009
[36] Z. D. Wang, Y. R. Liu, X. H. Liu. State estimation for jumping recurrent neural networks with discrete and distributed delays. Neural Networks, vol. 22, no. 1, pp. 41-48, 2009. doi:  10.1016/j.neunet.2008.09.015
[37] Y. Chen, W. X. Zheng. Stochastic state estimation for neural networks with distributed delays and Markovian jump. Neural Networks, vol. 25, pp. 14-20, 2012. doi:  10.1016/j.neunet.2011.08.002
[38] H. Huang, T. W. Huang, X. P. Chen. A mode-dependent approach to state estimation of recurrent neural networks with Markovian jumping parameters and mixed delays. Neural Networks, vol. 46, pp. 50-61, 2013. doi:  10.1016/j.neunet.2013.04.014
[39] R. Sakthivel, P. Vadivel, K. Mathiyalagan, A. Arunkumar, M. Sivachitra. Design of state estimator for bidirectional associative memory neural networks with leakage delays. Information Sciences, vol. 296, pp. 263-274, 2015. doi:  10.1016/j.ins.2014.10.063
[40] H. Huang, T. W. Huang, X. P. Chen. Guaranteed H performance state estimation of delayed static neural networks. IEEE Transactions on Circuits Systems Ⅱ: Express Briefs, vol. 60, no. 6, pp. 371-375, 2013. doi:  10.1109/TCSII.2013.2258258
[41] Q. H. Duan, H. Y. Su, Z. G. Wu. H state estimation of static neural networks with time-varying delay. Neurocomputing, vol. 97, pp. 16-21, 2012. doi:  10.1016/j.neucom.2012.05.021
[42] H. N. Wu, J. W. Wang, P. Shi. A delay decomposition approach to L2-L1 fllter design for stochastic systems with time-varying delay. Automatica, vol. 47, no. 7, pp. 1482- 1488, 2011. doi:  10.1016/j.automatica.2011.02.021