An LMI based chaotic passivity analysis on memristive neural networks for memductance function

• Autorzy: Suvetha R. , Prakash P.

Identyfikatory

DOI

10.2478/candc-2024-0022

• Języki publikacji: EN

Abstrakty

EN

This paper is devoted to passivity analysis for a class of chaotic memristive neural networks with distinct memductance approach, subject to actuator failures. Based on the existence of memristor, actuators and activation function, it was possible for the proposed model to stay in a stable state and reach the critical point by designing a strong reliable state-feedback controller. The qualitative analysis of this model can be developed using differential inclusion theory in the sense of Fillipov’s solution with suitable Lyapunov functional to acquire the results in terms of linear matrix inequalities (LMIs). Considering the known and unknown actuators cases, some sufficient conditions are derived for both statedependent switched system and state-dependent continuous system based on passivity theory along with its chaotic phenomena. The reliable state-feedback controller is designed to guarantee that the considered closed loop system is internally stable by adopting the stabilizing control law. Finally, numerical examples are presented to demonstrate theoretical results via graphical illustrations.

Słowa kluczowe

EN

passivity; memristive neural networks; actuator failure; linear matrix inequality; reliable controller

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2024
• Tom: Vol. 53, No. 4
• Strony: 533--568
• Opis fizyczny: Bibliogr. 38 poz., rys.

Twórcy

autor

Suvetha R.

• Department of Mathematics, Periyar University, Salem, 636-011, India

autor

Prakash P.

• Department of Mathematics, Periyar University, Salem, 636-011, India

Bibliografia

• 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.
• Bevelevich, V. (1968) Classical Network Synthesis. Van Nostrand, New York.
• Boyd, S., Ghaoui, L. E., Feron, E. and Balakrishnan, V. (1994) Linear Matrix Inequalities in System and Control Theory. SIAM, Philadelphia.
• 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.
• 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.
• Chua, L. (1971) Memristor-the missing circuit element. IEEE Transactions on Circuit Theory, 18, 507-519.
• 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.
• 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.
• Gu, Y., Shen, M., Ren, Y. and Liu, H. (2020) H1 finite-time control of unknown uncertain systems with actuator failure. Applied Mathematics and Computation, 383, 125375.
• 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.
• 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.
• 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.
• 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.
• 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.
• Padmaja, N. and Balasubramaniam, P. (2022) Results on passivity and design of passive controller for fuzzy neural networks with additive timevarying delays. Soft Computing, 26, 9911-9925.
• Pershin, Y. V. and Ventra, M. D. (2010) Experimental demonstration of associative memory with memristive neural networks. Neural Networks, 23, 881-886.
• 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.
• Rajchakit, G. and Sriraman, R. (2021) Robust passivity and stability analysis of uncertain complex-valued impulsive neural networks with timevarying delays. Neural Processing Letters, 53, 581-606.
• 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.
• 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.
• 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.
• 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.
• Suresh, R. and Manivannan, A. (2021) Robust stability analysis of delayed stochastic neural networks via Wirtinger based integral inequality. Neural Computation, 33, 227-243.
• Strukov, D. B., Snider, G. S., Stewart, D. R. and Williams, R. S. (2008) The missing memristor found. Nature. 453, 80-83.
• 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.
• 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.
• 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.
• 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
• Wang, Y., Cao, Y., Guo, Z. andWen, S. (2020) Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse. Applied Mathematics and Computation, 369, 124838.
• 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.
• 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.
• Xiao, J. and Zeng, Z. (2020) Finite-time passivity of neural networks with time varying delay. Journal of the Franklin Institute, 357, 2437-2456.
• 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.
• 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.
• 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.
• Zhang, G., Hu, J. and Shen, Y. (2015) New results on synchronization control of delayed memristive neural networks. Non-linear Dynamics, 81, 1167-1178.
• Zhang, J., Li, M. and Raissi, T. (2020) Reliable control for positive switched systems with random nonlinearities. ISA Transactions, 108, 48-57.
• 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.