1. Ali, M.S. and J. Yogambigai, 2018. Passivity-based synchronization of stochastic switched complex dynamical networks with additive time-varying delays via impulsive control. Neurocomputing, 273: 209-221.
   CrossRef  |  Direct Link  |  
2. Yucel, E., M.S. Ali, N. Gunasekaran and S. Arik, 2017. Sampled-data filtering of Takagi-Sugeno fuzzy neural networks with interval time-varying delays. Fuzzy Sets Syst., 316: 69-81.
   CrossRef  |  Direct Link  |  
3. Yogambigai, J. and M.S. Ali, 2017. Exponential synchronization of switched complex dynamical networks with time-varying delay via periodically intermittent control. Int. J. Difference Equations, 12: 41-53.
   Direct Link  |  
4. Senan, S., M.S. Ali, R. Vadivel and S. Arik, 2017. Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays. Neural Networks, 86: 32-41.
   CrossRef  |  Direct Link  |  
5. Saravanakumar, R., M.S. Ali, C.K. Ahn, H.R. Karimi and P. Shi, 2017. Stability of Markovian jump generalized neural networks with interval time-varying delays. IEEE Trans. Neural Networks Learn. Syst., 28: 1840-1850.
   CrossRef  |  Direct Link  |  
6. Saravanakumar, R., G. Rajchakit, M.S. Ali, Z. Xiang and Y.H. Joo, 2017. Robust extended dissipativity criteria for discrete-time uncertain neural networks with time-varying delays. Neural Comput. Applic. 10.1007/s00521-017-2974-z.
   CrossRef  |  
7. Saravanakumar, R., G. Rajchakit, M.S. Ali and Y.H. Joo, 2017. Extended dissipativity of generalised neural networks including time delays. Int. J. Syst. Sci., 48: 2311-2320.
   CrossRef  |  Direct Link  |  
8. Saravanakumar, R., G. Rajchakit, M.S. Ali and Y.H. Joo, 2017. Exponential dissipativity criteria for generalized bam neural networks with variable delays. Int. J. Syst. Sci. 10.1007/s00521-017-3224-0.
   CrossRef  |  
9. Meenakshi, K. and M.S. Ali, 2017. Mixed H∞ and passivity filtering for discrete time neural networks with interval time-varying delay. Int. J. Pure Applied Math., 113: 75-83.
10. Arslan, E., R. Vadivel, M.S. Ali and S. Arik, 2017. Event-triggered H∞ filtering for delayed neural networks via sampled-data. Neural Networks, 91: 11-21.
    CrossRef  |  Direct Link  |  
11. Arslan, E., M.S. Ali and S. Saravanan, 2017. Finite-time stability of stochastic cohen–grossberg neural networks with markovian jumping parameters and distributed time-varying delays. Neural Process. Lett., 46: 71-81.
    CrossRef  |  Direct Link  |  
12. Ali, M.S., S. Saravanan, M.E. Rani, S. Elakkia, J. Cao, A. Alsaedi and T. Hayat, 2017. Asymptotic stability of cohen-grossberg BAM neutral type neural networks with distributed time varying delays. Neural Process. Lett. 10.1007/s11063-017-9622-6.
    CrossRef  |  
13. Ali, M.S., S. Saravanan and Q. Zhu, 2017. Non-fragile finite-time H_∞ state estimation of neural networks with distributed time-varying delay. J. Franklin Inst., 354: 7566-7584.
    CrossRef  |  Direct Link  |  
14. Ali, M.S., S. Saravanan and Q. Zhu, 2017. Finite-time stability of neutral-type neural networks with random time-varying delays. Int. J. Syst. Sci., 48: 3279-3295.
    CrossRef  |  Direct Link  |  
15. Ali, M.S., S. Saravanan and J. Cao, 2017. Finite-time boundedness, L₂-gain analysis and control of Markovian jump switched neural networks with additive time-varying delays. Nonlinear Analysis: Hybrid Syst., 23: 27-43.
    CrossRef  |  
16. Ali, M.S., R. Vadivel and R. Saravanakumar, 2017. Event-triggered state estimation for Markovian jumping impulsive neural networks with interval time-varying delays. Int. J. Control. 10.1080/00207179.2017.1350884.
    CrossRef  |  
17. Ali, M.S., R. Saravanakumar, C.K. Ahn and H.R. Karimi, 2017. Stochastic H_∞ filtering for neural networks with leakage delay and mixed time-varying delays. Inform. Sci., 388: 118-134.
    CrossRef  |  Direct Link  |  
18. Ali, M.S., P. Balasubramaniam and Q. Zhu, 2017. Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays. Int. J. Machine Learn. Cybernetics, 8: 263-273.
    CrossRef  |  Direct Link  |  
19. Ali, M.S., N. Gunasekaran and Q. Zhu, 2017. State estimation of T-S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control. Fuzzy Sets Syst., 306: 87-104.
    CrossRef  |  Direct Link  |  
20. Ali, M.S., N. Gunasekaran and M.E. Rani, 2017. Robust stability of hopfield delayed neural networks via an augmented LK functional. Neurocomputing, 234: 198-204.
    CrossRef  |  Direct Link  |  
21. Ali, M.S., N. Gunasekaran and B. Aruna, 2017. Design of sampled-data control for multiple-time delayed generalised neural networks based on delay-partitioning approach. Int. J. Syst. Sci., 48: 2794-2810.
    CrossRef  |  Direct Link  |  
22. Ali, M.S., J. Yogambigai and C.A.O. Jinde, 2017. Synchronization of master-slave markovian switching complex dynamical networks with time-varying delays in nonlinear function via sliding mode control. Acta Math. Sci., 37: 368-384.
    CrossRef  |  Direct Link  |  
23. Ali, M.S. and S. Saravanan, 2017. Finite-time stability for memristor based uncertain neural networks with time-varying delays-via average dwell time approach. Chin. J. Phys., 55: 1953-1971.
    CrossRef  |  Direct Link  |  
24. Ali, M.S. and S. Saravanan, 2017. Finite-time stability for memristor based switched neural networks with time-varying delays via average dwell time approach. Neurocomputing. 10.1016/j.neucom.2017.10.003.
    CrossRef  |  
25. Ali, M.S. and R. Vadivel, 2017. Decentralized event-triggered exponential stability for uncertain delayed genetic regulatory networks with markov jump parameters and distributed delays. Neural Process. Lett. 10.1007/s11063-017-9695-2.
    CrossRef  |  
26. Ali, M.S. and N. Gunasekaran, 2017. State estimation of static neural networks with interval time-varying delays and sampled-data control. Comput. Applied Math. 10.1007/s40314-017-0470-9.
    CrossRef  |  
27. Ali, M.S. and J. Yogambigai, 2017. Finite-time robust stochastic synchronization of uncertain Markovian complex dynamical networks with mixed time-varying delays and reaction-diffusion terms via impulsive control. J. Franklin Inst., 354: 2415-2436.
    CrossRef  |  Direct Link  |  
28. Ali, M.S. and J. Yogambigai, 2017. Exponential stability of semi-markovian switching complex dynamical networks with mixed time varying delays and impulse control. Neural Process. Lett., 46: 113-133.
    CrossRef  |  Direct Link  |  
29. Saravanakumar, R. and M.S. Ali, 2016. Robust H_∞ control for uncertain Markovian jump systems with mixed delays. Chin. Phys. B, Vol. 25. .
    Direct Link  |  
30. Ali, M.S., S. Saravanan and S. Arik, 2016. Finite-time H1 filtering for switched neural networks with time-varying delays. Neurocomputing, 207: 580-589.
31. Ali, M.S., R. Saravanakumar, J. Cao and H. Huang, 2016. H_∞ state estimation of generalized neural networks with interval time-varying delays. Int. J. Syst. Sci., 47: 3888-3899.
    CrossRef  |  Direct Link  |  
32. Ali, M.S., R. Saravanakumar and S. Arik, 2016. Novel H_∞ state estimation of static neural networks with interval time-varying delays via augmented Lyapunov-Krasovskii functional. Neurocomputing, 171: 949-954.
    CrossRef  |  Direct Link  |  
33. Ali, M.S., R. Saravanakumar and M. Hua, 2016. H_∞ state estimation of stochastic neural networks with mixed time-varying delays. Softcomputing, 20: 3475-3487.
34. Ali, M.S., R. Saravanakumar and J. Cao, 2016. New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays. Neurocomputing, 171: 1533-1547.
    CrossRef  |  Direct Link  |  
35. Ali, M.S., P. Balasubramaniam, F.A. Rihan and S. Lakshmanan, 2016. Stability of stochastic takagi-sugeno fuzzy cohen-grossberg BAM neural networks with mixed time-varying delays. Complexity, 21: 143-154.
36. Ali, M.S., N. Gunasekaran, C.K. Ahn and P. Shi, 2016. Sampled-data stabilization for fuzzy genetic regulatory networks with leakage delays. IEEE/ACM Trans. Comput. Biol. Bioinform. 10.1109/TCBB.2016.2606477.
    CrossRef  |  
37. Ali, M.S., N. Gunasekaran and R. Saravanakumar, 2016. Design of passivity and passification for delayed neural networks with Markovian jump parameters via non-uniform sampled-data control. Neural Comput. Applic. 10.1007/s00521-016-2682-0.
    CrossRef  |  
38. Ali, M.S., 2016. Passivity analysis of uncertain stochastic neural networks with discrete and distributed time-varying delays. Int. J. Robotics Automation, 1: 29-43.
39. Ali, M.S. and S. Saravanan, 2016. Robust finite-time H_∞ control for a class of uncertain switched neural networks of neutral-type with distributed time varying delays. Neurocomputing, 177: 454-468.
    CrossRef  |  Direct Link  |  
40. Ali, M.S. and S. Saravanan, 2016. Finite-time L₂-gain analysis for switched neural networks with time-varying delay. Neural Comput. Applic. 10.1007/s00521-016-2498-y.
    CrossRef  |  
41. Ali, M.S. and R. Saravanakumar, 2016. Improved H_∞ performance analysis of uncertain Markovian jump systems with overlapping time‐varying delays. Complexity, 21: 460-477.
    Direct Link  |  
42. Ali, M.S. and J. Yogambigai, 2016. Synchronization of complex dynamical networks with hybrid coupling delays on time scales by handling multitude Kronecker product terms. Applied Math. Comput., 291: 244-258.
    CrossRef  |  Direct Link  |  
43. Ali, M.S. and M.E. Rani, 2016. Passivity analysis of stochastic neural networks with leakage delay and Markovian jumping parameters. Neurocomputing, 218: 139-145.
44. Ali, M.S., S. Arik and R. Saravanakumar, 2015. Delay-dependent stability criteria of uncertain Markovian jump neural networks with discrete interval and distributed time-varying delays. Neurocomputing, 158: 167-173.
    CrossRef  |  Direct Link  |  
45. Ali, M.S., R. Saravanakumar and Q. Zhu, 2015. Less conservative delay-dependent H_∞ control of uncertain neural networks with discrete interval and distributed time-varying delays. Neurocomputing, 166: 84-95.
    CrossRef  |  Direct Link  |  
46. Ali, M.S., 2015. Stochastic stability of uncertain recurrent neural networks with Markovian jumping parameters. Acta Math. Sci., 35: 1122-1136.
    CrossRef  |  Direct Link  |  
47. Ali, M.S., 2015. Stability of Markovian jumping recurrent neural networks with discrete and distributed time-varying delays. Neurocomputing, 149: 1280-1285.
    CrossRef  |  Direct Link  |  
48. Ali, M.S. and R. Saravanakumar, 2015. Robust H_∞ control of uncertain systems with two additive time-varying delays. Chin. Phys. B, Vol. 24. .
    Direct Link  |  
49. Ali, M.S. and R. Saravanakumar, 2015. Augmented Lyapunov approach to H_∞ state estimation of static neural networks with discrete and distributed time-varying delays. Chin. Phys. B, Vol. 24. .
    Direct Link  |  
50. Ali, M.S. and M.E. Rani, 2015. Passivity analysis of uncertain stochastic neural networks with time-varying delays and Markovian jumping parameters. Network: Comput. Neural Syst., 26: 73-96.
    Direct Link  |  
51. Ali, M.S., 2014. Stability analysis of Markovian jumping stochastic cohen-Grossberg neural networks with discrete and distributed time varying delays. Chin. Phys. B, Vol. 23. .
    Direct Link  |  
52. Ali, M.S., 2014. Robust stability of stochastic uncertain recurrent neural networks with Markovian jumping parameters and time-varying delays. Int. J. Mach. Learn. Cybernetics, 5: 13-22.
    CrossRef  |  Direct Link  |  
53. Ali, M.S., 2014. Robust stability of stochastic fuzzy impulsive recurrent neural networks with\time-varying delays. Iran. J. Fuzzy Syst., 11: 1-13.
    Direct Link  |  
54. Ali, M.S. and R. Saravanakumar, 2014. Novel delay-dependent robust H_∞ control of uncertain systems with distributed time-varying delays. Applied Math. Comput., 249: 510-520.
    CrossRef  |  Direct Link  |  
55. Ali, M.S. and R. Saravanakumar, 2014. Improved delay-dependent robust H_∞ control of an uncertain stochastic system with interval time-varying and distributed delays. Chin. Phys. B, Vol. 23. .
    Direct Link  |  
56. Ali, M.S., 2012. On exponential stability of neutral delay differential system with nonlinear uncertainties. Commun. Nonlinear Sci. Numerical Simulation, 17: 2595-2601.
    CrossRef  |  Direct Link  |  
57. Ali, M.S., 2012. Novel delay-dependent stability analysis of Takagi-Sugeno fuzzy uncertain neural networks with time varying delays. Chin. Phys. B, Vol. 21. .
    Direct Link  |  
58. Balasubramaniam, P. and M.S. Ali, 2011. Stochastic stability of uncertain fuzzy recurrent neural networks with Markovian jumping parameters. Int. J. Comput. Math., 88: 892-904.
    CrossRef  |  Direct Link  |  
59. Balasubramaniam, P. and M.S. Ali, 2011. Stability analysis of takagi-sugeno fuzzy cohen-grossberg BAM neural networks with discrete and distributed time-varying delays. Math. Comput. Modell., 53: 151-160.
    CrossRef  |  Direct Link  |  
60. Balasubramaniam, P. and M.S. Ali, 2011. Stability analysis of Takagi-Sugeno stochastic fuzzy Hopfield neural networks with discrete and distributed time varying delays. Neurocomputing, 74: 1520-1526.
    CrossRef  |  Direct Link  |  
61. Balasubramaniam, P. and M.S. Ali, 2011. Global asymptotic stqbility of dyzzy cellular neural networks with multiple discrete and distributed time varying delays. Commun. Nonlinear Sci. Numerical Simulation, 6: 2907-2916.
62. Ali, M.S. and M. Marudai, 2011. Global asymptotic stability of stochastic Discrete-time neural networks with time-varying delays. Math. Comput. Modell., 54: 1979-1988.
63. Balasubramaniam, P., M.S. Ali and S. Arik, 2010. Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple time-varying delays. Expert Syst. Applic., 37: 7737-7744.
    CrossRef  |  Direct Link  |  
64. Balasubramaniam, P. and M.S. Ali, 2010. Robust stability of uncertain fuzzy cellular neural networks with time-varying delays and reaction diffusion terms. Neurocomputing, 74: 439-446.
    CrossRef  |  Direct Link  |  
65. Balasubramaniam, P. and M.S. Ali, 2010. Robust exponential stability of uncertain fuzzy Cohen-Grossberg neural networks with time-varying delays. Fuzzy Sets Syst., 161: 608-618.
    CrossRef  |  Direct Link  |  
66. Ali, M.S. and P. Balasubramaniam, 2010. Exponential stability of time delay differential systems. Int. J. Comput. Math., 87: 1363-1373.
67. Balasubramaniam, P., M.S. Ali and J.H. Kim, 2009. Faedo-Galerkin approximate solutions for stochastic semilinear integrodifferential equations. Comput. Math. Applic., 58: 48-57.
    CrossRef  |  Direct Link  |  
68. Ali, M.S. and P. Balasubramaniam, 2009. Stability analysis of uncertain fuzzy Hopfield neural networks with time delays. Commun. Nonlinear Sci. Numerical Simulation, 14: 2776-2783.
    CrossRef  |  Direct Link  |  
69. Ali, M.S. and P. Balasubramaniam, 2009. Robust stability of uncertain fuzzy Cohen-Grossberg BAM neural networks with time-varying delays. Expert Syst. Applic., 36: 10583-10588.
    CrossRef  |  Direct Link  |  
70. Ali, M.S. and P. Balasubramaniam, 2009. Global exponential stability of uncertain fuzzy BAM neural networks with time-varying delays Chaos. Solitons Fractals, 42: 2191-2199.
71. Ali, M.S. and P. Balasubramaniam, 2009. Exponential stability of uncertain stochastic fuzzy BAM neural networks with time-varying delays. Neurocomputing, 72: 1347-1354.
    CrossRef  |  Direct Link  |  
72. Rakkiyappan, R., P. Balasubramaniam and S. Lakshmanan, 2008. Robust stability results for uncertain stochastic neural networks with discrete interval and distributed time-varying delays. Physics Lett. A, 372: 5290-5298.
    CrossRef  |  Direct Link  |