[
Anbuvithya, R., Mathiyalagan, K., Sakthivel, R. and Prakash, P. (2016) Passivity of memristor-based BAM neural networks with different memductance and uncertain delays. Cognitive Neurodynamics, 10, 339-351.
]Search in Google Scholar
[
Bevelevich, V. (1968) Classical Network Synthesis. Van Nostrand, New York.
]Search in Google Scholar
[
Boyd, S., Ghaoui, L. E., Feron, E. and Balakrishnan, V. (1994) Linear Matrix Inequalities in System and Control Theory. SIAM, Philadelphia.
]Search in Google Scholar
[
Chandrasekar, A., Rakkiyappan, R. and Li, X. (2016) Effects of bounded and unbounded leakage time-varying delays in memristor-based recurrent neural networks with different memductance functions. Neurocomputing, 202, 67-83.
]Search in Google Scholar
[
Chen, L., Li, T., Chen, Y., Wu, R. and Ge, S. (2019) Robust passivity and feedback passification of a class of uncertain fractional-order linear systems. International Journal of Systems Science, 50, 1149-1162.
]Search in Google Scholar
[
Chua, L. (1971) Memristor-the missing circuit element. IEEE Transactions on Circuit Theory, 18, 507-519.
]Search in Google Scholar
[
Ding, Z., Yang, L., Ye, Y., Li, S. and Huang, Z. (2023) Passivity and passification of fractional-order memristive neural networks with time delays. ISA Transactions, 137, 314-322.
]Search in Google Scholar
[
Ge, C., Park, J. H., Hua, C. and Shi, C. (2019) Robust passivity analysis for uncertain neural networks with discrete and distributed time-varying delays. Neurocomputing, 364, 330-337.
]Search in Google Scholar
[
Gu, Y., Shen, M., Ren, Y. and Liu, H. (2020) H∞ finite-time control of unknown uncertain systems with actuator failure. Applied Mathematics and Computation, 383, 125375.
]Search in Google Scholar
[
Guo, Z., Wang, J. and Yan, Z. (2014) Passivity and passification of memristor based recurrent neural networks with time-varying delays. IEEE Transactions on Neural Networks and Learning Systems, 25, 2099-2109.
]Search in Google Scholar
[
Huang, Y. L., Qiu, S. H. and Ren, S. Y. (2020) Finite-time synchronization and passivity of coupled memristive neural networks. International Journal of Control, 93, 2824-2837.
]Search in Google Scholar
[
Junfeng, Z., Li, M. and Raissi, T. (2020) Reliable actuator fault control of positive switched systems with double switchings. Asian Journal of Control, 23, 1831-1844.
]Search in Google Scholar
[
Li, Y., Zhong, S., Cheng, J., Shi, K. and Ren, J. (2016) New passivity criteria for uncertain neural networks with time-varying delay. Neurocomputing, 171, 1003-1012.
]Search in Google Scholar
[
Liu, J. and Xu, R. (2016) Passivity analysis of memristive neural networks with mixed time-varying delays and different state-dependent memductance functions. Advances in Difference Equations, 245, 1-22.
]Search in Google Scholar
[
Padmaja, N. and Balasubramaniam, P. (2022) Results on passivity and design of passive controller for fuzzy neural networks with additive time-varying delays. Soft Computing, 26, 9911-9925.
]Search in Google Scholar
[
Pershin, Y. V. and Ventra, M. D. (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Networks, 23, 881-886.
]Search in Google Scholar
[
Qiu, S. B., Liu, X. G., Wang, F. X. and Chen, Q. (2019) Stability and passivity analysis of discrete-time linear systems with time-varying delay. Systems and Control Letters, 134, 104543.
]Search in Google Scholar
[
Rajchakit, G. and Sriraman, R. (2021) Robust passivity and stability analysis of uncertain complex-valued impulsive neural networks with time-varying delays. Neural Processing Letters, 53, 581-606.
]Search in Google Scholar
[
Rajavel, S., Samidurai, R., Kilbert, S.A.J., Cao, J. and Alsaedi, A. (2018) Non-fragile mixed H1 and passivity control for neural networks with successive time-varying delay components. Nonlinear Analysis: Modelling and Control, 23, 159-181.
]Search in Google Scholar
[
Sau, N. H., Thuan, M. V. and Huyen, N. (2020) Passivity analysis of fractional order neural networks with time-varying delay based on LMI approach. Circuits, Systems, and Signal Processing, 39, 5906-5925.
]Search in Google Scholar
[
Shafiya, M. and Nagamani, G. (2022) New finite-time passivity criteria for delayed fractional-order neural networks based on Lyapunov function approach. Chaos, Solitons and Fractals, 158, 112005.
]Search in Google Scholar
[
Song, Q. and Wang, Z. (2010) New results on passivity analysis of uncertain neural networks with time-varying delays. International Journal of Computer Mathematics, 87, 668-678.
]Search in Google Scholar
[
Suresh, R. and Manivannan, A. (2021) Robust stability analysis of delayed stochastic neural networks via Wirtinger based integral inequality. Neural Computation, 33, 227-243.
]Search in Google Scholar
[
Strukov, D. B., Snider, G. S., Stewart, D. R. and Williams, R. S. (2008) The missing memristor found. Nature. 453, 80-83.
]Search in Google Scholar
[
Suresh, R., Syed Ali, M. and Saroha, S. (2023) Global exponential stability of memristor based uncertain neural networks with time-varying delays via Lagrange sense. Journal of Experimental and Theoretical Artificial Intelligence, 35, 275-288.
]Search in Google Scholar
[
Vadivel, R., Hammachukiattikul, P., Zhu, Q. and Gunasekaran, N. (2023) Event-triggered synchronization for stochastic delayed neural networks: Passivity and passification case. Asian Journal of Control, 25, 2681-2698.
]Search in Google Scholar
[
Wang, L., Zeng, Z., Ge, M. F. and Hu, J. (2018) Global stabilization analysis of inertial memristive recurrent neural networks with discrete and distributed delays. Neural Networks, 105, 65-74.
]Search in Google Scholar
[
Wang, X. B., Tang, H. A., Xia, Q., Zhao, Q. and Tan, G. Y. (2022) Feedback control for passivity of memristor based multiple weighted coupled neural networks. Discrete Dynamics in Nature and Society, 6920495. https://doi.org/10.1155/2022/6920495
]Search in Google Scholar
[
Wang, Y., Cao, Y., Guo, Z. and Wen, S. (2020) Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse. Applied Mathematics and Computation, 369, 124838.
]Search in Google Scholar
[
Wu, A. and Zeng, Z. (2014) Passivity analysis of memristive neural networks with different memductance functions. Communications in Nonlinear Science and Numerical Simulation, 19, 274-285.
]Search in Google Scholar
[
Xiao, J. Zhong, S. and Li, Y. (2015) New passivity criteria for memristive uncertain neural networks with leakage and time-varying delays, ISA Transactions, 59, 133-148.
]Search in Google Scholar
[
Xiao, J. and Zeng, Z. (2020) Finite-time passivity of neural networks with time varying delay. Journal of the Franklin Institute, 357, 2437-2456.
]Search in Google Scholar
[
Yang, H., Cocquempot, V. and Jiang, B. (2008) Fault tolerance analysis for switched systems via global passivity. IEEE Transactions on Circuits and Systems II: Express Briefs, 55, 1279-1283.
]Search in Google Scholar
[
Yang, B., Wang, J., Hao, M. and Zeng, H. (2018) Further results on passivity analysis for uncertain neural networks with discrete and distributed delays. Information Sciences, 431, 77-86.
]Search in Google Scholar
[
Zeng, H. B., Park, J. H. and Shen, H. (2015) Robust passivity analysis of neural networks with discrete and distributed delays. Neurocomputing, 149, 1092-1097.
]Search in Google Scholar
[
Zhang, G., Hu, J. and Shen, Y. (2015) New results on synchronization control of delayed memristive neural networks. Non-linear Dynamics, 81, 1167-1178.
]Search in Google Scholar
[
Zhang, J., Li, M. and Raissi, T. (2020) Reliable control for positive switched systems with random nonlinearities. ISA Transactions, 108, 48-57.
]Search in Google Scholar
[
Zhang, H., Ma, Q., Lu, J., Chu, Y. and Li, Y. (2021) Synchronization control of neutral-type neural networks with sampled-data via adaptive event-triggered communication scheme. Journal of the Franklin Institute, 358, 1999-2014.
]Search in Google Scholar
