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2020 – today
- 2024
[j41]K. Sri Raja Priyanka, Gnaneswaran Nagamani
:
Non-fragile projective synchronization of delayed discrete-time neural networks via generalized weighted summation inequality. Appl. Math. Comput. 479: 128885 (2024)- [j40]K. Sri Raja Priyanka, G. Soundararajan, Ardak Kashkynbayev, Gnaneswaran Nagamani:
Co-existence of robust output-feedback synchronization and anti-synchronization of delayed discrete-time neural networks with its application. Comput. Appl. Math. 43(1): 77 (2024) - [j39]G. Soundararajan, Gnaneswaran Nagamani, Ardak Kashkynbayev:
Exponential H∞ filtering for complex-valued uncertain discrete-time neural networks with time-varying delays. Commun. Nonlinear Sci. Numer. Simul. 128: 107595 (2024) - [j38]B. Adhira, Gnaneswaran Nagamani:
Extended dissipative criteria for delayed semi-discretized competitive neural networks. Neural Process. Lett. 56(2): 122 (2024) - 2023
- [j37]B. Adhira, Gnaneswaran Nagamani, Dafik:
Non-fragile extended dissipative synchronization control of delayed uncertain discrete-time neural networks. Commun. Nonlinear Sci. Numer. Simul. 116: 106820 (2023) - [j36]K. Sri Raja Priyanka, G. Soundararajan, Ardak Kashkynbayev, Gnaneswaran Nagamani:
Exponential H∞ synchronization and anti-synchronization of delayed discrete-time complex-valued neural networks with uncertainties. Math. Comput. Simul. 207: 301-321 (2023) - [j35]G. Soundararajan, Gnaneswaran Nagamani:
Non-fragile output-feedback synchronization for delayed discrete-time complex-valued neural networks with randomly occurring uncertainties. Neural Networks 159: 70-83 (2023) - [j34]B. Adhira, Gnaneswaran Nagamani:
Extended Dissipativity Performance for the Delayed Discrete-Time Neural Networks with Observer-Based Control. Neural Process. Lett. 55(2): 927-947 (2023) - [j33]A. Karnan, Gnaneswaran Nagamani:
Synchronization of Uncertain Neural Networks with Additive Time-Varying Delays and General Activation Function. Neural Process. Lett. 55(4): 4951-4971 (2023) - 2022
- [j32]M. Shafiya, Gnaneswaran Nagamani:
Extended dissipativity criterion for fractional-order neural networks with time-varying parameter and interval uncertainties. Comput. Appl. Math. 41(3) (2022) - [j31]B. Adhira, Gnaneswaran Nagamani, G. Soundararajan:
Exponential extended dissipative performance for delayed discrete-time neural networks under memoryless resilient-based observer design. J. Frankl. Inst. 359(11): 5750-5777 (2022) - [j30]M. Shafiya, Gnaneswaran Nagamani, Dafik:
Global synchronization of uncertain fractional-order BAM neural networks with time delay via improved fractional-order integral inequality. Math. Comput. Simul. 191: 168-186 (2022) - [j29]A. Karnan, Gnaneswaran Nagamani:
Non-fragile state estimation for memristive cellular neural networks with proportional delay. Math. Comput. Simul. 193: 217-231 (2022) - [j28]Karthik Chinnasamy, Gnaneswaran Nagamani, Ramasamy Subramaniyam, Dafik:
Robust stabilization of T-S fuzzy systems via improved integral inequality. Soft Comput. 26(1): 349-360 (2022) - 2021
- [j27]Gnaneswaran Nagamani, A. Karnan, G. Soundararajan:
Delay-Dependent and Independent State Estimation for BAM Cellular Neural Networks with Multi-Proportional Delays. Circuits Syst. Signal Process. 40(7): 3179-3203 (2021) - [j26]Gnaneswaran Nagamani, Karthik Chinnasamy, Ramasamy Subramaniam, Quanxin Zhu:
Robust Exponential Stability Analysis for Stochastic Systems With Actuator Faults Using Improved Weighted Relaxed Integral Inequality. IEEE Trans. Syst. Man Cybern. Syst. 51(6): 3346-3357 (2021) - 2020
- [j25]Gnaneswaran Nagamani, Young Hoon Joo, G. Soundararajan, Reza Mohajerpoor:
Robust event-triggered reliable control for T-S fuzzy uncertain systems via weighted based inequality. Inf. Sci. 512: 31-49 (2020) - [j24]Gnaneswaran Nagamani, M. Shafiya, G. Soundararajan, Mani Prakash:
Robust state estimation for fractional-order delayed BAM neural networks via LMI approach. J. Frankl. Inst. 357(8): 4964-4982 (2020) - [j23]Gnaneswaran Nagamani, C. Karthik, Young Hoon Joo:
Event-triggered observer-based sliding mode control for T-S fuzzy systems via improved relaxed-based integral inequality. J. Frankl. Inst. 357(14): 9543-9567 (2020) - [j22]Gnaneswaran Nagamani, G. Soundararajan, Ramasamy Subramaniam, Muhammad Azeem:
Robust extended dissipativity analysis for Markovian jump discrete-time delayed stochastic singular neural networks. Neural Comput. Appl. 32(13): 9699-9712 (2020) - [j21]Gnaneswaran Nagamani, M. Shafiya, G. Soundararajan:
An LMI Based State Estimation for Fractional-Order Memristive Neural Networks with Leakage and Time Delays. Neural Process. Lett. 52(3): 2089-2108 (2020) - [j20]Gnaneswaran Nagamani, Ganesan Soundara Rajan, Quanxin Zhu:
Exponential State Estimation for Memristor-Based Discrete-Time BAM Neural Networks With Additive Delay Components. IEEE Trans. Cybern. 50(10): 4281-4292 (2020) - [j19]Ganesan Soundara Rajan, Gnaneswaran Nagamani:
State-Feedback Filtering for Delayed Discrete-Time Complex-Valued Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 31(11): 4726-4736 (2020)
2010 – 2019
- 2019
- [j18]Gnaneswaran Nagamani, Young Hoon Joo, Han Sol Kim:
A linear matrix inequality-based extended dissipativity criteria for linear systems with additive time-varying delays. IFAC J. Syst. Control. 10: 100070 (2019) - [j17]Gnaneswaran Nagamani, C. Karthik, G. Soundararajan:
Observer-based exponential stabilization for time-delay systems via augmented weighted integral inequality. J. Frankl. Inst. 356(16): 9023-9042 (2019) - 2018
- [j16]Thirunavukkarasu Radhika, Gnaneswaran Nagamani, Quanxin Zhu, S. Ramasamy, R. Saravanakumar:
Further results on dissipativity analysis for Markovian jump neural networks with randomly occurring uncertainties and leakage delays. Neural Comput. Appl. 30(11): 3565-3579 (2018) - 2017
- [j15]Gnaneswaran Nagamani, Thirunavukkarasu Radhika, Padmini Gopalakrishnan:
Dissipativity and passivity analysis of Markovian jump impulsive neural networks with time delays. Int. J. Comput. Math. 94(7): 1479-1500 (2017) - [j14]Gnaneswaran Nagamani, S. Ramasamy, Anke Meyer-Baese:
Robust dissipativity and passivity based state estimation for discrete-time stochastic Markov jump neural networks with discrete and distributed time-varying delays. Neural Comput. Appl. 28(4): 717-735 (2017) - [j13]S. Ramasamy, Gnaneswaran Nagamani, Thirunavukkarasu Radhika:
Further Results on Dissipativity Criterion for Markovian Jump Discrete-Time Neural Networks with Two Delay Components Via Discrete Wirtinger Inequality Approach. Neural Process. Lett. 45(3): 939-965 (2017) - [j12]Gnaneswaran Nagamani, Thirunavukkarasu Radhika, Quanxin Zhu:
An Improved Result on Dissipativity and Passivity Analysis of Markovian Jump Stochastic Neural Networks With Two Delay Components. IEEE Trans. Neural Networks Learn. Syst. 28(12): 3018-3031 (2017) - 2016
- [j11]Gnaneswaran Nagamani, S. Ramasamy:
Stochastic dissipativity and passivity analysis for discrete-time neural networks with probabilistic time-varying delays in the leakage term. Appl. Math. Comput. 289: 237-257 (2016) - [j10]Gnaneswaran Nagamani, S. Ramasamy, Pagavathigounder Balasubramaniam:
Robust dissipativity and passivity analysis for discrete-time stochastic neural networks with time-varying delay. Complex. 21(3): 47-58 (2016) - [j9]Gnaneswaran Nagamani, Thirunavukkarasu Radhika, Pagavathi Balasubramaniam:
A delay decomposition approach for robust dissipativity and passivity analysis of neutral-type neural networks with leakage time-varying delay. Complex. 21(5): 248-264 (2016) - [j8]Gnaneswaran Nagamani, S. Ramasamy:
Dissipativity and passivity analysis for uncertain discrete-time stochastic Markovian jump neural networks with additive time-varying delays. Neurocomputing 174: 795-805 (2016) - [j7]Gnaneswaran Nagamani, S. Ramasamy:
Dissipativity and passivity analysis for discrete-time T-S fuzzy stochastic neural networks with leakage time-varying delays based on Abel lemma approach. J. Frankl. Inst. 353(14): 3313-3342 (2016) - [j6]Gnaneswaran Nagamani, Thirunavukkarasu Radhika:
Dissipativity and Passivity Analysis of Markovian Jump Neural Networks with Two Additive Time-Varying Delays. Neural Process. Lett. 44(2): 571-592 (2016) - 2012
- [j5]Pagavathigounder Balasubramaniam, Gnaneswaran Nagamani:
A Delay Decomposition Approach to Delay-Dependent Robust Passive Control for Takagi-Sugeno Fuzzy Nonlinear Systems. Circuits Syst. Signal Process. 31(4): 1319-1341 (2012) - [j4]Pagavathigounder Balasubramaniam, Gnaneswaran Nagamani:
Global robust passivity analysis for stochastic fuzzy interval neural networks with time-varying delays. Expert Syst. Appl. 39(1): 732-742 (2012) - 2011
- [j3]Pagavathigounder Balasubramaniam, Gnaneswaran Nagamani:
A delay decomposition approach to delay-dependent passivity analysis for interval neural networks with time-varying delay. Neurocomputing 74(10): 1646-1653 (2011) - [j2]Pagavathigounder Balasubramaniam, Gnaneswaran Nagamani:
Global Robust Passivity Analysis for Stochastic Interval Neural Networks with Interval Time-Varying Delays and Markovian Jumping Parameters. J. Optim. Theory Appl. 149(1): 197-215 (2011) - 2010
- [j1]Pagavathigounder Balasubramaniam, Gnaneswaran Nagamani, R. Rakkiyappan:
Global Passivity Analysis of Interval Neural Networks with Discrete and Distributed Delays of Neutral Type. Neural Process. Lett. 32(2): 109-130 (2010)
Coauthor Index
aka: Ganesan Soundara Rajan
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