Global exponential stability of memristor based uncertain neural networks with time-varying delays via Lagrange sense

References

• Ali, M. S., & Balasubramaniam, P. (2009). Stability analysis of uncertain fuzzy Hopfield neural networks with time delays. Communications in Nonlinear Science and Numerical Simulation, 14(6), 2776–2783. https://doi.org/10.1016/j.cnsns.2008.09.024
   Web of Science ®Google Scholar
• Anbalagan, P., Ramachandran, R., Cao, J., Rajchakit, G., & Lim, C. P. (2019). Global robust synchronization of fractional order complex valued neural networks with mixed time varying delays and impulses. International Journal of Control, Automation and Systems, 17(2), 509–520. https://doi.org/10.1007/s12555-017-0563-7
   Web of Science ®Google Scholar
• Aouiti, C., & Assali, E. A. (2019). Stability analysis for a class of impulsive high-order Hopfield neural networks with leakage time-varying delays. Neural Computing and Applications, 31(11), 7781–7803. https://doi.org/10.1007/s00521-018-3585-z
   Web of Science ®Google Scholar
• Balasubramaniam, P., & Lakshmanan, S. (2009). Delay-range dependent stability criteria for neural networks with Markovian jumping parameters. Nonlinear Analysis: Hybrid Systems, 3(4), 749–756. https://doi.org/10.1016/j.nahs.2009.06.012
   Web of Science ®Google Scholar
• Boyd, S. P., Ghaoui, L. E., Feron, E., & Balakrishnan, V. (1994). Linear Matrix Inequality in System and Control Theory. SIAM, (Philadelphia). https://doi.org/10.1137/1.9781611970777
   Google Scholar
• Cao, Y. Y., Sun, Y. X., & Cheng, C. (1998). Delay-dependent robust stabilization of uncertain systems with multiple state delays. IEEE Transactions on Automatic Control, 43(11), 1608–1612. https://doi.org/10.1109/9.728880
   Web of Science ®Google Scholar
• Chanthorn, P., Rajchakit, G., Humphries, U., Kaewmesri, P., Sriraman, R., & Lim, C. P. (2020a). A Delay-Dividing Approach to Robust Stability of Uncertain Stochastic Complex-Valued Hopfield Delayed Neural Networks. Symmetry, 12(5), 683. https://doi.org/10.3390/sym12050683
   Google Scholar
• Chanthorn, P., Rajchakit, G., Thipcha, J., Emharuethai, C., Sriraman, R., Lim, C. P., & Ramachandran, R. (2020b). Robust Stability of Complex-Valued Stochastic Neural Networks with Time-Varying Delays and Parameter Uncertainties. Mathematics, 8(5), 742. https://doi.org/10.3390/math8050742
   Web of Science ®Google Scholar
• Chen, B., Zhong, L., & Ping, X. (2010). Transient chaos in smooth memristor oscillator. Chinese Physics B, 19(3), 030510. https://doi.org/10.1088/1674-1056/19/3/030510
   Web of Science ®Google Scholar
• Chen, C., Li, L., Peng, H., Yang, Y., & Li, T. (2017). Finite-time synchronization of memristor-based neural networks with mixed delays. Neurocomputing, 235, 83–89. https://doi.org/10.1016/j.neucom.2016.12.061
   Web of Science ®Google Scholar
• Chen, J., Park, J. H., & Xu, S. (2020). Stability Analysis for Delayed Neural Networks With an Improved General Free-Matrix-Based Integral Inequality. IEEE Transactions on Neural Networks and Learning Systems, 31(2), 675–684. https://doi.org/10.1109/TNNLS.2019.2909350
   PubMed Web of Science ®Google Scholar
• Chua, L. (1971). Memristor-The missing circuit element. IEEE Transactions on Circuit Theory, 18(5), 507–519. https://doi.org/10.1109/TCT.1971.1083337
   Google Scholar
• Duan, S. K., Hu, X. F., Dong, Z. K., Wang, L. D., & Mazumder, P. (2015). Memristor-Based Cellular Nonlinear/Neural Network: Design, Analysis, and Applications. IEEE Transactions on Neural Networks and Learning Systems, 26(6), 1202–1213. https://doi.org/10.1109/TNNLS.2014.2334701
   PubMed Web of Science ®Google Scholar
• Gao, Z. M., He, Y., & Wu, M. (2019). Improved stability criteria for the neural networks with time-varying delay via new augmented Lyapunov–Krasovskii functional. Applied Mathematics and Computation, 349(9), 258–269. https://doi.org/10.1016/j.amc.2018.12.026
   Web of Science ®Google Scholar
• Gu, Y., Yu, Y., & Wang, H. (2019). Projective synchronization for fractional-order memristor-based neural networks with time delays. Neural Computing and Applications, 31(10), 6039–6054. https://doi.org/10.1007/s00521-018-3391-7
   Web of Science ®Google Scholar
• Gunasekaran, N., Syed Ali, M., & Pavithra, S. (2020). Finite-Time Performance State Estimation of Recurrent Neural Networks with Sampled-Data Signals. Neural Processing Letters, 51(2), 1379–1392. https://doi.org/10.1007/s11063-019-10114-9
   Web of Science ®Google Scholar
• Hien, L. V., & Trinh, H. (2016). Exponential stability of time-delay systems via new weighted integral inequalities. Applied Mathematics and Computation, 275, 335–344. https://doi.org/10.1016/j.amc.2015.11.076
   Web of Science ®Google Scholar
• Ho, Y., Huang, G. M., & Li, P. (2011). Dynamical properties and design analysis for nonvolatile memristor memories. IEEE Transactions on Circuits and Systems I, 58(4), 724–736. https://doi.org/10.1109/TCSI.2010.2078710
   Google Scholar
• Huang, C. (2020). Exponential stability of inertial neural networks involving proportional delays and non-reduced order method. Journal of Experimental and Theoretical Artificial Intelligence, 32(1), 133–146. https://doi.org/10.1080/0952813X.2019.1635654
   Web of Science ®Google Scholar
• Humphries, U., Rajchakit, G., Kaewmesri, P., Chanthorn, P., Sriraman, R., Samidurai, R., & Lim, C. P. (2020). Stochastic Memristive Quaternion-Valued Neural Networks with Time Delays: An Analysis on Mean Square Exponential Input-to-State Stability. Mathematics, 8(5), 815. https://doi.org/10.3390/math8050815
   Web of Science ®Google Scholar
• Jia, J., Huang, X., Li, Y., Cao, J., & Alsaedi, A. (2020). Global Stabilization of Fractional-Order Memristor-Based Neural Networks With Time Delay. IEEE Transactions on Neural Networks and Learning Systems, 31(3), 997–1009. https://doi.org/10.1109/TNNLS.2019.2915353
   PubMed Web of Science ®Google Scholar
• Lakshmanan, S., Lim, C. P., Prakash, M., Nahavandi, S., & Balasubramaniam, P. (2017). Neutral-type of delayed inertial neural networks and their stability analysis using the LMI approach. Neurocomputing, 230(12), 243–250. https://doi.org/10.1016/j.neucom.2016.12.020
   Web of Science ®Google Scholar
• Li, N., & Cao, J. (2018). Synchronization criteria for multiple memristor-based neural networks with time delay and inertial term.  Science China Technological Sciences, 61(4), 611–622. https://doi.org/10.1007/s11431-017-9189-3
   Web of Science ®Google Scholar
• Li, Y., Luo, B., Liu, D., Yang, Z., & Zhu, Y. (2020). Adaptive synchronization of memristor-based neural networks with discontinuous activations. Neurocomputing, 381(11), 196–206. https://doi.org/10.1016/j.neucom.2019.11.018
   Web of Science ®Google Scholar
• Liao, X., Luo, Q., Zeng, Z. G., & Guo, Y. X. (2008). Global exponential stability in Lagrange sense for recurrent neural networks with time delays. Nonlinear Analysis: Real World Applications, 9(4), 1535–1557. https://doi.org/10.1016/j.nonrwa.2007.03.018
   Web of Science ®Google Scholar
• Liu, X., & Li, Z. (2019). Global $\mu$ -Stability of Quaternion-Valued Neural Networks With Unbounded and Asynchronous Time-Varying Delays. IEEE Access, 7, 9128–9141. https://doi.org/10.1109/ACCESS.2019.2891721
   Web of Science ®Google Scholar
• Mani, P., Rajan, R., Shanmugam, L., & Joo, Y. H. (2019). Adaptive control for fractional order induced chaotic fuzzy cellular neural networks and its application to image encryption. Information Sciences, 491(22), 74–89. https://doi.org/10.1016/j.ins.2019.04.007
   Web of Science ®Google Scholar
• Muralisankar, S., Manivannan, A., & Balasubramaniam, P. (2012). Robust stability criteria for uncertain neutral type stochastic system with Takagi–Sugeno fuzzy model and Markovian jumping parameters. Communications in Nonlinear Science and Numerical Simulation, 17(10), 3876–3893. https://doi.org/10.1016/j.cnsns.2012.02.011
   Web of Science ®Google Scholar
• Pershin, Y., & Di Ventra, M. (2010). Experimental demonstration of associative memory with memristive neural networks. Neural Networks, 23(7), 881–886. https://doi.org/10.1016/j.neunet.2010.05.001
   PubMed Web of Science ®Google Scholar
• Pratap, A., Raja, R., Cao, J., Rajchakit, G., & Alsaadi, F. E. (2018). Further synchronization in finite time analysis for time-varying delayed fractional order memristive competitive neural networks with leakage delay. Neurocomputing, 317(47), 110–126. https://doi.org/10.1016/j.neucom.2018.08.016
   Web of Science ®Google Scholar
• Rajchakit, G., Chanthorn, P., Kaewmesri, P., Sriraman, R., & Lim, C. P. (2020a). Global Mittag–Leffler Stability and Stabilization Analysis of Fractional-Order Quaternion-Valued Memristive Neural Networks. Mathematics, 8(3), 422. https://doi.org/10.3390/math8030422
   Web of Science ®Google Scholar
• Rajchakit, G., Chanthorn, P., Niezabitowski, M., Raja, R., Baleanu, D., & Pratap, A. (2020b). Impulsive effects on stability and passivity analysis of memristor-based fractional-order competitive neural networks. Neurocomputing, 417(47), 290–301. https://doi.org/10.1016/j.neucom.2020.07.036
   Web of Science ®Google Scholar
• Ratnavelu, K., Kalpana, M., & Balasubramaniam, P. (2016). Asymptotic stability of Markovian switching genetic regulatory networks with leakage and mode-dependent time delays. Journal of the Franklin Institute, 353(7), 1615–1638. https://doi.org/10.1016/j.jfranklin.2016.01.015
   Web of Science ®Google Scholar
• Rekasius, Z. (1963). Lagrange stability of nonlinear feedback systems. IEEE Transactions on Automatic Control, 8(2), 160–163. https://doi.org/10.1109/TAC.1963.1105547
   Web of Science ®Google Scholar
• Seuret, A., & Gouaisbaut, F. (2013). Wirtinger-based integral inequality: Application to time-delay systems. Automatica, 49(9), 2860–2866. https://doi.org/10.1016/j.automatica.2013.05.030
   Web of Science ®Google Scholar
• Shen, W., Zhang, X., & Wang, Y. (2020). Stability analysis of high order neural networks with proportional delays. Neurocomputing, 372(2), 33–39. https://doi.org/10.1016/j.neucom.2019.09.019
   Web of Science ®Google Scholar
• Song, Q., Shu, H., Zhao, Z., Liu, Y., & Alsaadi, F. E. (2017). Lagrange stability analysis for complex-valued neural networks with leakage delay and mixed time-varying delays. Neurocomputing, 244(26), 33–41. https://doi.org/10.1016/j.neucom.2017.03.015
   Web of Science ®Google Scholar
• Strukov, D. B., Snider, G. S., Stewart, D. R., & Williams, R. S. (2008). The missing memristor found. Nature, 453(7191), 80–83. https://doi.org/10.1038/nature06932
   PubMed Web of Science ®Google Scholar
• Su, L., & Zhou, L. (2019). Exponential synchronization of memristor-based recurrent neural networks with multi-proportional delays. Neural Computing and Applications, 31(11), 7907–7920. https://doi.org/10.1007/s00521-018-3569-z
   Web of Science ®Google Scholar
• Sun, J., Shen, Y., Yin, Q., & Xu, C. (2013). Compound synchronization of four memristor chaotic oscillator systems and secure communication. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(1), 013140. https://doi.org/10.1063/1.4794794
   PubMed Web of Science ®Google Scholar
• Syed Ali, M., Saravanakumar, R., & Cao, J. (2016). New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays. Neurocomputing, 171(1), 1533–1547. https://doi.org/10.1016/j.neucom.2015.07.101
   Web of Science ®Google Scholar
• Syed Ali, M., Saravanan, S., & Palanisamy, L. (2019). Stochastic finite-time stability of reaction-diffusion Cohen–Grossberg neural networks with time-varying delays. Chinese Journal of Physics, 57(1), 314–328. https://doi.org/10.1016/j.cjph.2018.09.039
   Web of Science ®Google Scholar
• Tu, Z., Cao, J., & Hayat, T. (2016). Global exponential stability in Lagrange sense for inertial neural networks with time-varying delays. Neurocomputing, 171(1), 524–531. https://doi.org/10.1016/j.neucom.2015.06.078
   Web of Science ®Google Scholar
• Vadivel, R., Syed Ali, M., & Joo, Y. H. (2021). Robust H∞ performance for discrete time T-S fuzzy switched memristive stochastic neural networks with mixed time-varying delays. Journal of Experimental and Theoretical Artificial Intelligence, 33(1), 79–107. https://doi.org/10.1080/0952813X.2020.1725649
   Web of Science ®Google Scholar
• Wang, J., Liu, F., & Qin, S. (2019). Global exponential stability of uncertain memristor-based recurrent neural networks with mixed time delays. International Journal of Machine Learning and Cybernetics, 10(4), 743–755. https://doi.org/10.1007/s13042-017-0759-4
   Web of Science ®Google Scholar
• Wang, J., & Tian, L. (2017). Global Lagrange stability for inertial neural networks with mixed time-varying delays. Neurocomputing, 235(17), 140–146. https://doi.org/10.1016/j.neucom.2017.01.007
   Web of Science ®Google Scholar
• Wang, W., Wang, X., Luo, X., & Yuan, M. (2018). Finite-Time Projective Synchronization of Memristor-Based BAM Neural Networks and Applications in Image Encryption. IEEE Access, 6, 56457–56476. https://doi.org/10.1109/ACCESS.2018.2872745
   Web of Science ®Google Scholar
• Wang, Y., Xie, L., & De Souza, C. E. (1992). Robust control of a class of uncertain nonlinear systems. Systems & Control Letters, 19(2), 139–149. https://doi.org/10.1016/0167-6911(92)90097-C
   Web of Science ®Google Scholar
• Wu, A., Wen, S., & Zeng, Z. G. (2012). Synchronization control of a class of memristor-based recurrent neural networks. Information Sciences, 183(1), 106–116. https://doi.org/10.1016/j.ins.2011.07.044
   Web of Science ®Google Scholar
• Xu, C., & Li, P. (2018). Periodic Dynamics for Memristor-based Bidirectional Associative Memory Neural Networks with Leakage Delays and Time-varying Delays. International Journal of Control, Automation and Systems, 16(2), 535–549. https://doi.org/10.1007/s12555-017-0235-7
   Web of Science ®Google Scholar
• Yang, N., Yu, Y., Zhong, S., Wang, X., Shi, K., & Cai, J. (2020). Exponential Stability of Markovian Jumping Memristor-Based Neural Networks via Event-Triggered Impulsive Control Scheme. IEEE Access, 8, 32564–32574. https://doi.org/10.1109/ACCESS.2020.2974040
   Web of Science ®Google Scholar
• Yang, Y., Mathew, J., Chakraborty, R. S., Ottavi, M., & Pradhan, D. K. (2016). Low cost memristor associative memory design for full and partial matching applications. IEEE Transactions on Nanotechnology, 15(3), 527–538. https://doi.org/10.1109/TNANO.2016.2553438
   Web of Science ®Google Scholar
• Yue, D. (2004). Robust stabilization of uncertain systems with unknown input delay. Automatica, 40(2), 331–336. https://doi.org/10.1016/j.automatica.2003.10.005
   Web of Science ®Google Scholar
• Zhang, C. K., He, Y., Jiang, L., Lin, W. J., & Wu, M. (2017a). Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach. Applied Mathematics and Computation, 294, 102–120. https://doi.org/10.1016/j.amc.2016.08.043
   Web of Science ®Google Scholar
• Zhang, G., & Zeng, Z. (2018). Exponential stability for a class of memristive neural networks with mixed time-varying delays. Applied Mathematics and Computation, 321, 544–554. https://doi.org/10.1016/j.amc.2017.11.022
   Web of Science ®Google Scholar
• Zhang, W., Huang, T., He, X., & Li, C. (2017b). Global exponential stability of inertial memristor-based neural networks with time-varying delays and impulses. Neural Networks, 95(11), 102–109. https://doi.org/10.1016/j.neunet.2017.03.012
   Web of Science ®Google Scholar
• Zhang, X., Han, Q., & Zeng, Z. (2018). Hierarchical Type Stability Criteria for Delayed Neural Networks via Canonical Bessel–Legendre Inequalities. IEEE Transactions on Cybernetics, 48(5), 1660–1671. https://doi.org/10.1109/TCYB.2017.2776283
   PubMed Web of Science ®Google Scholar
• Zhenga, M., Li, L., Peng, H., Xiao, J., Yang, Y., Zhang, Y., & Zhao, H. (2018). Finite-time stability and synchronization of memristor-based fractional-order fuzzy cellular neural networks. Communications in Nonlinear Science and Numerical Simulation, 59(6), 272–291. https://doi.org/10.1016/j.cnsns.2017.11.025
   Web of Science ®Google Scholar