Abstract
This article discusses the dissipativity analysis of stochastic generalized neural network (NN) models with Markovian jump parameters and time-varying delays. In practical applications, most of the systems are subject to stochastic perturbations. As such, this study takes a class of stochastic NN models into account. To undertake this problem, we first construct an appropriate Lyapunov–Krasovskii functional with more system information. Then, by employing effective integral inequalities, we derive several dissipativity and stability criteria in the form of linear matrix inequalities that can be checked by the MATLAB LMI toolbox. Finally, we also present numerical examples to validate the usefulness of the results.
Funding source: Thailand Research Fund
Award Identifier / Grant number: RSA6280004
-
Author contribution: Funding acquisition, G.R.; Conceptualization, G.R.; Software, G.R. and R.S. (R. Sriraman); Formal analysis, G.R.; Methodology, G.R.; Supervision, R.S. (R. Samidurai); Writing–original draft, G.R.; Validation, G.R.; Writing– review and editing, G.R. All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: This research work was supported by the Thailand Research Research Grant Fund (RSA6280004) and Maejo University, Thailand.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
[1] J. Cao, “Global asymptotic stability of neural networks with transmission delays,” Int. J. Syst. Sci., vol. 31, pp. 1313–1316, 2000. https://doi.org/10.1080/00207720050165807.Search in Google Scholar
[2] S. Arik, “An analysis of global asymptotic stability of delayed cellular neural networks,” IEEE Trans. Neural Network., vol. 13, pp. 1239–1242, 2012. https://doi.org/10.1109/tnn.2002.1031957.Search in Google Scholar PubMed
[3] Z. Zhao, Q. Song, and S. He, “Passivity analysis of stochastic neural networks with time-varying delays and leakage delay,” Neurocomputing, vol. 125, pp. 22–27, 2014. https://doi.org/10.1016/j.neucom.2012.08.049.Search in Google Scholar
[4] O. M. Kwon, S. M. Lee, and J. H. Park, “Improved delay-dependent exponential stability for uncertain stochastic neural networks with time-varying delays,” Phys. Lett. A, vol. 374, pp. 1232–1241, 2010. https://doi.org/10.1016/j.physleta.2010.01.007.Search in Google Scholar
[5] H. B. Zeng, J. H. Park, C. F. Zhang, and W. Wang, “Stability and dissipativity analysis of static neural networks with interval time-varying delay,” J. Franklin Inst., vol. 352, pp. 1284–1295, 2015. https://doi.org/10.1016/j.jfranklin.2014.12.023.Search in Google Scholar
[6] P. Muthukumar, K. Subramanian, and S. Lakshmanan, “Robust finite time stabilization analysis for uncertain neural networks with leakage delay and probabilistic time-varying delays,” J. Franklin Inst., vol. 353, pp. 4091–4113, 2016. https://doi.org/10.1016/j.jfranklin.2016.07.006.Search in Google Scholar
[7] Y. Chen, Z. Wang, Y. Liu, and F. E. Alsaadi, “Stochastic stability for distributed delay neural networks via augmented Lyapunov-Krasovskii functionals,” Appl. Math. Comput., vol. 338, pp. 869–881, 2018. https://doi.org/10.1016/j.amc.2018.05.059.Search in Google Scholar
[8] H. Huang, T. Huang, and Y. Cao, “Reduced-order filtering of delayed static neural networks with Markovian jumping parameters,” IEEE Trans. Neural Network Learn. Syst., vol. 29, pp. 5606–5618, 2018. https://doi.org/10.1109/tnnls.2018.2806356.Search in Google Scholar PubMed
[9] S. Jiao, H. Shen, Y. Wei, X. Huang, and Z. Wang, “Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays,” Appl. Math. Comput., vol. 336, pp. 338–350, 2018. https://doi.org/10.1016/j.amc.2018.05.013.Search in Google Scholar
[10] G. Chen, J. Xia, and G. Zhuang, “Delay-dependent stability and dissipativity analysis of generalized neural networks with Markovian jump parameters and two delay components,” J. Franklin Inst., vol. 353, pp. 2137–2158, 2016. https://doi.org/10.1016/j.jfranklin.2016.02.020.Search in Google Scholar
[11] R. Samidurai, R. Manivannan, C. K. Ahn, and H. R. Karimi, “New criteria for stability of generalized neural networks including Markov jump parameters and additive time delays,” IEEE Trans. Syst., Man, and Cybern., Syst., vol. 48, pp. 485–499, 2018. https://doi.org/10.1109/tsmc.2016.2609147.Search in Google Scholar
[12] G. Stamov, I. Stamova, and J. Alzabut, “Global exponential stability for a class of impulsive BAM neural networks with distributed delays,” Appl. Math. Inf. Sci., vol. 7, pp. 1539–1546, 2013. https://doi.org/10.12785/amis/070438.Search in Google Scholar
[13] G. Stamov and J. Alzabut, “Almost periodic solutions of impulsive integro-differential neural networks,” Math. Model Anal., vol. 15, pp. 505–516, 2010. https://doi.org/10.3846/1392-6292.2010.15.505-516.Search in Google Scholar
[14] J. Alzabut, “Existence and stability of neutral-type BAM neural networks with time delays in the neutral and leakage terms on time scales,” Global J. Pure Appl. Math., vol. 13, pp. 589–616, 2017.Search in Google Scholar
[15] A. Pratap, R. Raja, J. Cao, J. Alzabut, and C. Huang, “Finite-time synchronization criterion of graph theory perspective fractional-order coupled discontinuous neural networks,” Adv. Differ. Equ., vol. 2020, p. 97, 2020. https://doi.org/10.1186/s13662-020-02551-x.Search in Google Scholar
[16] R. Sakthivel, A. Arunkumar, K. Mathiyalagan, and S. Marshal Anthoni, “Robust passivity analysis of fuzzy Cohen-Grossberg BAM neural networks with time-varying delays,” Appl. Math. Comput., 275, pp. 213–228, 2011. https://doi.org/10.1016/j.amc.2011.09.024.Search in Google Scholar
[17] E. K. Boukas, Z. K. Liu, and G. X. Liu, “Delay-dependent robust stability and H∞ control of jump linear systems with time-delay,” Int. J. Contr., vol. 74, pp. 329–340, 2010. https://doi.org/10.1080/00207170010008752.Search in Google Scholar
[18] Y. Y. Cao, J. Lam, and L. S. Hu, “Delay-dependent stochastic stability and H∞ analysis for time-delay systems with Markovian jumping parameters,” J. Franklin Inst., vol. 340, pp. 423–434, 2003. https://doi.org/10.1016/j.jfranklin.2003.09.001.Search in Google Scholar
[19] Q. Zhu and J. Cao, “Exponential stability of stochastic neural networks with both Markovian jump parameters and mixed time delays,” IEEE Trans. Syst., Man, and Cybern., Part B, vol. 41, pp. 341–353, 2011. https://doi.org/10.1109/tsmcb.2010.2053354.Search in Google Scholar
[20] Q. Zhu and J. Cao, “Robust exponential stability of Markovian jump impulsive stochastic Cohen–Grossberg neural networks with mixed time delays,” IEEE Trans. Neural Network., vol. 21, pp. 1314–1325, 2010. https://doi.org/10.1109/TNN.2010.2054108.Search in Google Scholar PubMed
[21] H. Tan, M. Hua, J. Chen, and J. Fei, “Stability analysis of stochastic Markovian switching static neural networks with asynchronous mode-dependent delays,” Neurocomputing, vol. 151, pp. 864–872, 2015. https://doi.org/10.1016/j.neucom.2014.10.009.Search in Google Scholar
[22] S. Zhu, M. Shen, and C. C. Lim, “Robust input-to-state stability of neural networks with Markovian switching in presence of random disturbances or time delays,” Neurocomputing, vol. 249, pp. 245–252, 2017. https://doi.org/10.1016/j.neucom.2017.04.004.Search in Google Scholar
[23] C. Pradeep, A. Chandrasekar, M. Murugesu, and R. Rakkiyappan, “Robust stability analysis of stochastic neural networks with Markovian jumping parameters and probabilistic time-varying delays,” Complexity, vol. 21, pp. 59–72, 2014. https://doi.org/10.1002/cplx.21630.Search in Google Scholar
[24] S. Blythe, X. Mao, and X. Liao, “Stability of stochastic delay neural networks,” J. Franklin Inst., vol. 338, pp. 481–495, 2001. https://doi.org/10.1016/s0016-0032(01)00016-3.Search in Google Scholar
[25] Y. Chen and W. Zheng, “Stability analysis of time-delay neural networks subject to stochastic perturbations,” IEEE Trans. Cybern., vol. 43, pp. 2122–2134, 2013. https://doi.org/10.1109/tcyb.2013.2240451.Search in Google Scholar
[26] C. Wang and Y. Shen, “Delay-dependent non-fragile robust stabilization and H∞ control of uncertain stochastic systems with time-varying delay and nonlinearity,” J. Franklin Inst., vol. 348, pp. 2174–2190, 2011. https://doi.org/10.1016/j.jfranklin.2011.06.010.Search in Google Scholar
[27] G. Liu, S. X. Yang, Y. Chai, W. Feng, and W. Fu, “Robust stability criteria for uncertain stochastic neural networks of neutral-type with interval time-varying delays,” Neural Comput. Appl., vol. 22, pp. 349–359, 2013. https://doi.org/10.1007/s00521-011-0696-1.Search in Google Scholar
[28] R. Yang, H. Gao, and P. Shi, “Novel robust stability criteria for stochastic Hopfield neural networks with time delays,” IEEE Trans. Syst., Man, and Cybern., Part B, vol. 39, pp. 467–474, 2009. https://doi.org/10.1109/tsmcb.2008.2006860.Search in Google Scholar
[29] S. Zhu and Y. Shen, “Passivity analysis of stochastic delayed neural networks with Markovian switching,” Neurocomputing, vol. 74, pp. 1754–1761, 2011. https://doi.org/10.1016/j.neucom.2011.02.010.Search in Google Scholar
[30] J. C. Willems, “Dissipative dynamical systems part I: General theory,” Arch. Ration. Mech. Anal., vol. 45, pp. 321–351, 1972. https://doi.org/10.1007/bf00276493.Search in Google Scholar
[31] D. L. Hill and P. J. Moylan, “Dissipative dynamical systems: basic input-output and state properties,” J. Franklin Inst., vol. 309, pp. 327–357, 1980. https://doi.org/10.1016/0016-0032(80)90026-5.Search in Google Scholar
[32] J. H. Park, H. Shen, X. H. Chang, and T. H. Lee, Recent Advances in Control and Filtering of Dynamic Systems with Constrained Signals, Cham, Switzerland, Springer, 2018.Search in Google Scholar
[33] J. Xia, G. Chen, J. H. Park, H. Shen, and G. Zhuang, “Dissipativity-based sampled-data control for fuzzy switched Markovian jump systems,” IEEE Trans. Fuzzy Syst., 2020, https://doi.org/10.1109/TFUZZ.2020.2970856.Search in Google Scholar
[34] Z. G. Wu, J. H. Park, H. Su, and J. Chu, “Robust dissipativity analysis of neural networks with time-varying delay and randomly occurring uncertainties,” Nonlinear Dynam., vol. 69, pp. 1323–1332, 2012. https://doi.org/10.1007/s11071-012-0350-1.Search in Google Scholar
[35] Z. Feng and J. Lam, “Stability and dissipativity analysis of distributed delay cellular neural networks,” IEEE Trans. Neural Network., vol. 22, pp. 976–981, 2011. https://doi.org/10.1109/TNN.2011.2128341.Search in Google Scholar PubMed
[36] R. Samidurai, R. Sriraman, and S. Zhu, “Stability and dissipativity analysis for uncertain Markovian jump systems with random delays via new approach,” Int. J. Syst. Sci., vol. 50, pp. 1609–1625, 2019. https://doi.org/10.1080/00207721.2019.1618942.Search in Google Scholar
[37] R. Raja, U. K. Raja, R. Samidurai, and A. Leelamani, “Dissipativity of discrete-time BAM stochastic neural networks with Markovian switching and impulses,” J. Franklin Inst., vol. 350, pp. 3217–3247, 2013. https://doi.org/10.1016/j.jfranklin.2013.08.003.Search in Google Scholar
[38] R. Samidurai and R. Sriraman, “Robust dissipativity analysis for uncertain neural networks with additive time-varying delays and general activation functions,” Math. Comput. Simulat., vol. 155, pp. 201–216, 2019. https://doi.org/10.1016/j.matcom.2018.03.010.Search in Google Scholar
[39] R. Manivannan, R. Samidurai, and Q. Zhu, “Further improved results on stability and dissipativity analysis of static impulsive neural networks with interval time-varying delays,” J. Franklin Inst., vol. 354, pp. 6312–6340, 2017. https://doi.org/10.1016/j.jfranklin.2017.07.040.Search in Google Scholar
[40] C. C. Hua, C. N. Long, and X. P. Guan, “New results on stability analysis of neural networks with time-varying delays,” Phys. Lett. A, vol. 352, pp. 335–340, 2006. https://doi.org/10.1016/j.physleta.2005.12.005.Search in Google Scholar
[41] Y. He, G. P. Liu, D. Rees, and M. Wu, “Stability analysis for neural networks with time-varying interval delay,” IEEE Trans. Neural Network., vol. 18, pp. 1850–1854, 2007. https://doi.org/10.1109/tnn.2006.888373.Search in Google Scholar
[42] T. Li, L. Guo, C. Sun, and C. Lin, “Further results on delay-dependent stability criteria of neural networks with time-varying delays,” IEEE Trans. Neural Network., vol. 19, pp. 726–730, 2008. https://doi.org/10.1109/tnn.2007.914162.Search in Google Scholar PubMed
[43] X. M. Zhang and Q. L. Han, “New Lyapunov–Krasovskii functionals for global asymptotic stability of delayed neural networks,” IEEE Trans. Neural Network., vol. 20, pp. 533–539, 2009. https://doi.org/10.1109/TNN.2009.2014160.Search in Google Scholar PubMed
[44] S. P. Xiao and X. M. Zhang, “New globally asymptotic stability criteria for delayed cellular neural networks,” IEEE Trans. Circuits Syst. II, vol. 56, pp. 659–663, 2009. https://doi.org/10.1109/tcsii.2009.2024244.Search in Google Scholar
[45] H. B. Zeng, Y. He, M. Wu, and C. F. Zhang, “Complete delay-decomposing approach to asymptotic stability for neural networks with time-varying delays,” IEEE Trans. Neural Network., vol. 22, pp. 806–812, 2011. https://doi.org/10.1109/TNN.2011.2111383.Search in Google Scholar PubMed
[46] C. Ge, C. Hua, and X. Guan, “New delay-dependent stability criteria for neural networks with time-varying delay using delay-decomposition approach,” IEEE Trans. Neural Network Learning Syst., vol. 25, pp. 1378–1383, 2014. https://doi.org/10.1109/tnnls.2013.2285564.Search in Google Scholar
[47] X. M. Zhang and Q. L. Han, “Global asymptotic stability for a class of generalized neural networks with interval time-varying delays,” IEEE Trans. Neural Network. Learning Syst., vol. 22, pp. 1180–1192, 2011. https://doi.org/10.1109/tnn.2011.2147331.Search in Google Scholar
[48] C. K. Zhang, Y. He, L. Jiang, Q. H. Wu, and M. Wu, “Delay-dependent stability criteria for generalized neural networks with two delay components,” IEEE Trans. Neural Network Learn. Syst., vol. 25, pp. 1263–1276, 2014. https://doi.org/10.1109/tnnls.2013.2284968.Search in Google Scholar
[49] H. B. Zeng, Y. He, M. Wu, and S. Xiao, “Stability analysis of generalized neural networks with time-varying delays via a new integral inequality,” Neurocomputing, vol. 161, pp. 148–154, 2015. https://doi.org/10.1016/j.neucom.2015.02.055.Search in Google Scholar
[50] B. Wang, J. Yan, J. Cheng, and S. Zhong, “New criteria of stability analysis for generalized neural networks subject to time-varying delayed signals,” Appl. Math. Comput., vol. 314, pp. 322–333, 2017. https://doi.org/10.1016/j.amc.2017.06.031.Search in Google Scholar
[51] H. D. Choi, C. K. Ahn, M. T. Lim, and M. K. Song, “Dynamic output-feedback H∞ control for active half-vehicle suspension systems with time-varying input delay,” Int. J. Contr. Autom. Syst., vol. 14, pp. 59–68, 2016. https://doi.org/10.1007/s12555-015-2005-8.Search in Google Scholar
[52] P. G. Park, S. Y. Lee, and W. I. Lee, “Auxiliary function-based integral inequalities for quadratic functions and their applications to time delay systems,” J. Franklin Inst., vol. 352, pp. 1378–1396, 2015. https://doi.org/10.1016/j.jfranklin.2015.01.004.Search in Google Scholar
[53] S. Rajavel, R. Samidurai, J. Cao, A. Alsaedi, and B. Ahmad, “Finite-time non-fragile passivity control for neural networks with time-varying delay,” Appl. Math. Comput., vol. 297, pp. 145–158, 2017. https://doi.org/10.1016/j.amc.2016.10.038.Search in Google Scholar
[54] J. Chen, S. Xu, W. Chen, B. Zhang, Q. Ma, and Y. Zou, “Two general integral inequalities and their applications to stability analysis for systems with time-varying delay,” Int. J. Robust Nonlinear Control, vol. 26, pp. 4088–4103, 2016. https://doi.org/10.1002/rnc.3551.Search in Google Scholar
[55] P. G. Park, J. W. Ko, and C. Jeong, “Reciprocally convex approach to stability of systems with time-varying delays,” Automatica, vol. 47, pp. 235–238, 2011. https://doi.org/10.1016/j.automatica.2010.10.014.Search in Google Scholar
© 2021 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Original Research Articles
- Slippage phenomenon in hydromagnetic peristaltic rheology with hall current and viscous dissipation
- Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters
- Numerical solutions for strain-softening surrounding rock under three-dimensional principal stress condition
- The exact Riemann solutions to an isentropic non-ideal dusty gas flow under a magnetic field
- Higher order approximation of biharmonic problem using the WEB-Spline based mesh-free method
- Solution of non-linear time fractional telegraph equation with source term using B-spline and Caputo derivative
- Nonlinear stability and numerical simulations for a reaction–diffusion system modelling Allee effect on predators
- Computational analysis of heat and mass transfer in a micropolar fluid flow through a porous medium between permeable channel walls
- 3D structure of single and multiple vortices in a flow under rotation
- Interaction solutions of a variable-coefficient Kadomtsev–Petviashvili equation with self-consistent sources
Articles in the same Issue
- Frontmatter
- Original Research Articles
- Slippage phenomenon in hydromagnetic peristaltic rheology with hall current and viscous dissipation
- Dissipativity analysis of delayed stochastic generalized neural networks with Markovian jump parameters
- Numerical solutions for strain-softening surrounding rock under three-dimensional principal stress condition
- The exact Riemann solutions to an isentropic non-ideal dusty gas flow under a magnetic field
- Higher order approximation of biharmonic problem using the WEB-Spline based mesh-free method
- Solution of non-linear time fractional telegraph equation with source term using B-spline and Caputo derivative
- Nonlinear stability and numerical simulations for a reaction–diffusion system modelling Allee effect on predators
- Computational analysis of heat and mass transfer in a micropolar fluid flow through a porous medium between permeable channel walls
- 3D structure of single and multiple vortices in a flow under rotation
- Interaction solutions of a variable-coefficient Kadomtsev–Petviashvili equation with self-consistent sources