#### ** Dr. Ramasamy Subramaniam**

**Qualification : Ph.D.**

### **Details of Educational Qualification:**

Course | Specialization | Group | College Name/University | Year of Passing |
---|---|---|---|---|

Ph.D. | Mathematical Sciences | Mathematical Sciences | The GandhigramRural Institute- DeemedUniversity, Dindigu | 2017 |

M.Phil. | Mathematics | M.Phil. | Periyar University, Salem | 2009 |

M.Sc. | Mathematics | M.Sc | Mahendra Arts and Science College, Namakkal | 2006 |

B.Sc. | Mathematics | B.Sc. | Thiruvalluvar Government Arts College, Rasipuram | 2003 |

### **My Publications**

S.No | Title of the Paper | Full Details of Journal Name / Conference Name, Volume number, page number, Date |
---|---|---|

1 | T-S fuzzy-based sliding mode control design for discrete-time nonlinear model and its applications. | Information Sciences, DOI:https://doi.org/10.1016/j.ins.2020.01.010, I.F=5.524. |

2 | Robust exponential stability analysis for stochastic systems with actuator faults using improved weighted relaxed integral inequality. | IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2019.2924327, I.F=7.351. |

3 | Robust extended dissipativity analysis for markovian jump discrete-time delayed stochastic singular neural networks. | Neural Computing and Applications, DOI:10.1007/s00521-019-04497-y, I.F=4.664. |

4 | Passivity-based fuzzy ISMC for wind energy conversion systems with pmsg. | IEEE Transactions on Systems, Man, and Cybernetics: Systems, DOI: 10.1109/TSMC.2019.2930743, I.F=7.351. |

5 | Further results on dissipativity analysis for markovian jump neural networks with randomly occurring uncertainties and leakage delays. | Neural Computing and Applications, 30(11), 3565–3579, I.F=4.664. |

6 | Robust dissipativity and passivity based state estimation for discrete-time stochastic markov jump neural networks with discrete and distributed time-varying delays. | Neural Computing and Applications, 28(4), 717–735, I.F=4.664. |

7 | Dissipativity and passivity analysis for discrete-time complex-valued neural networks with leakage delay and probabilistic time-varying delays. | International J ournal of Adaptive Control and Signal Processing, 31(6), 876–902, I.F=2.239. |

8 | Further results on dissipativity criterion for markovian jump discrete-time neural networks with two delay components via discrete wirtinger inequality approach. | Neural Processing Letters, 45(3), 939–965, I.F=2.591. |

9 | Dissipativity and passivity analysis for discrete-time t–s fuzzy stochastic neural networks with leakage time-varying delays based on abel lemma approach. | J ournal of the Franklin Institute, 353(14), 3313–3342, I.F=3.653. |

10 | Dissipativity and passivity analysis for uncertain discrete-time stochastic markovian jump neural networks with additive time-varying delays. | Neurocomputing, 174, 795–805, I.F=3.317. |

11 | Stochastic dissipativity and passivity analysis for discrete-time neural networks with probabilistic time-varying delays in the leakage term. | Applied Mathematics and Computation, 289, 237–257, I.F=3.092. |

12 | Robust dissipativity and passivity analysis for discrete-time stochastic neural networks with time-varying delay. | Complexity, 21(3),47–58, I.F=2.591. |

13 | State estimation for discrete-time neural networks with two additive time-varying delay components based on passivity theory. | Int J Pure Appl Math, 106(6), 131–141. |

14 | Robust dissipativity and passivity analysis for discrete-time stochastic t–s fuzzy cohen–grossberg markovian jump neural networks with mixed time delays. | Nonlinear Dynamics, 85(4), 2777–2799, I.F=3.464. |

15 | Dissipativity and passivity analysis for discrete-time complex-valued neural networks with time-varying delay. | Cogent Mathematics, 2(1), 1048580. |

16 | Passivity analysis for discrete-time neural networks with time-varying delay, | International Conference on Nonlinear Dynamical Systems (ICNDS 2016), Department of Mathematics, Bharathiar University, India, 2016. |

17 | Passivity analysis for discrete-time Stochastic neural networks with time-delay, | National Conference on Recent Developments in Di erential Equations and their Applications, Department of Mathematics, PSGR Krishnammal College for Women, India, 2016. |

18 | State estimation for discrete-time neural networks with two additive delays, | National Conference on Mathematical Modelling and Fuzzy Logic Applications, Department of Mathematics, RVS Technical Campus, India, 2016. |

19 | Passivity analysis for discrete-time complex valued neural networks with distributed delay, | National Seminar on Recent trends in Di erential Equations, Department of mathematics, Arulmigu Palaniandavar Arts College for Women, India, 2015. |