Keywords: distributed delays; leakage delay; passivity impulses; stochastic disturbances
@article{10_14736_kyb_2018_1_0003,
author = {Raj, Senthil and Ramachandran, Raja and Rajendiran, Samidurai and Cao, Jinde and Li, Xiaodi},
title = {Passivity analysis of uncertain stochastic neural network with leakage and distributed delays under impulsive perturbations},
journal = {Kybernetika},
pages = {3--29},
year = {2018},
volume = {54},
number = {1},
doi = {10.14736/kyb-2018-1-0003},
mrnumber = {3780953},
zbl = {06861611},
language = {en},
url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2018-1-0003/}
}
TY - JOUR AU - Raj, Senthil AU - Ramachandran, Raja AU - Rajendiran, Samidurai AU - Cao, Jinde AU - Li, Xiaodi TI - Passivity analysis of uncertain stochastic neural network with leakage and distributed delays under impulsive perturbations JO - Kybernetika PY - 2018 SP - 3 EP - 29 VL - 54 IS - 1 UR - http://geodesic.mathdoc.fr/articles/10.14736/kyb-2018-1-0003/ DO - 10.14736/kyb-2018-1-0003 LA - en ID - 10_14736_kyb_2018_1_0003 ER -
%0 Journal Article %A Raj, Senthil %A Ramachandran, Raja %A Rajendiran, Samidurai %A Cao, Jinde %A Li, Xiaodi %T Passivity analysis of uncertain stochastic neural network with leakage and distributed delays under impulsive perturbations %J Kybernetika %D 2018 %P 3-29 %V 54 %N 1 %U http://geodesic.mathdoc.fr/articles/10.14736/kyb-2018-1-0003/ %R 10.14736/kyb-2018-1-0003 %G en %F 10_14736_kyb_2018_1_0003
Raj, Senthil; Ramachandran, Raja; Rajendiran, Samidurai; Cao, Jinde; Li, Xiaodi. Passivity analysis of uncertain stochastic neural network with leakage and distributed delays under impulsive perturbations. Kybernetika, Tome 54 (2018) no. 1, pp. 3-29. doi: 10.14736/kyb-2018-1-0003
[1] Balasubramaniam, P., Nagamani, G., Rakkiyappan, R.: Passivity analysis for neural networks of neutral type with Markovian jumping parameters and time delay in the leakage term. Comm. Nonlinear Sci. Numerical Simul. 16 (2011), 4422-4437. | DOI | MR
[2] Boyd, S., Ghaoui, L., Feron, E., Balakrishnan, V.: Linear Matrix Inequalities in System and Control Theory. SIAM, Philadelphia 1994. | DOI | Zbl
[3] Cao, J., Li, R.: Fixed-time synchronization of delayed memristor-based recurrent neural networks. Science China Inform. Sci. 60 (2017), 032201. | DOI
[4] Cao, J., Rakkiyappan, R., Maheswari, K., Chandrasekar, A.: Exponential $H_{\infty}$ filtering analysis for discrete-time switched neural networks with random delays using sojourn probabilities. Science China Inform. Sci. 59(2016), 3, 387-402. | DOI
[5] Chen, Y., Wang, H., Xue, A., Lu, R.: Passivity analysis of stochastic time-delay neural networks. Nonlinear Dynamics 61 (2010), 71-82. | DOI | MR
[6] Gopalsamy, K.: Stability and Oscillations in Delay Differential Equations of Population Dynamics. Kluwer Academic Publishers, Dordrecht 1992. | DOI | MR
[7] Haykin, S.: Neural Networks: a Comprehensive Foundation (revised ed.). Upper Saddle River, Prentice-Hall, NJ 1998.
[8] Hu, M., Cao, J., Hu, A.: Exponential stability of discrete-time recurrent neural networks with time-varying delays in the leakage terms and linear fractional uncertainties. IMA J. Math. Control Inform. 31 (2014), 345-362. | DOI | MR
[9] Gu, K.: An integral inequality in the stability problem of time delay systems. In: Proc. 39th IEEE Conference on Decision Control 2000, pp. 2805-2810. | DOI
[10] He, Y., Wang, Q., Lin, C., Wu, M.: Delay-range-dependent stability for systems with time-varying delay. Automatica 43 (2007), 371-376. | DOI | MR
[11] Kwon, O., Lee, S., Park, Ju H.: Improved delay-dependent exponential stability for uncertain stochastic neural networks with time-varying delays. Physics Lett. A 374 (2010), 1232-1241. | DOI
[12] Kwon, O., Park, M., Park, Ju.H., Lee, S., Cha, E.: Improved approaches to stability criteria for neural networks with time-varying delays. J. Franklin Inst. 350 (2013), 2710-2735. | DOI | MR
[13] Li, X., Cao, J.: Delay-dependent stability of neural networks of neutral typewith time delay in the leakage term. Nonlinearity 23 (2010), 1709-1726. | DOI | MR
[14] Li, R., Cao, J.: Dissipativity analysis of memristive neural networks with time-varying delays and randomly occurring uncertainties. Math. Methods Appl. Sci. 39 (2016), 11, 2896-2915. | DOI | MR
[15] Li, R., Cao, J.: Stability analysis of reaction-diffusion uncertain memristive neural networks with time-varying delays and leakage term. Appl. Math. Comput. 278 (2016), 54-69. | DOI | MR
[16] Li, X., Fu, X.: Effect of leakage time-varying delay on stability of nonlinear differential systems. J. Franklin Inst. 350 (2013), 1335-1344. | DOI | MR
[17] Li, X., Rakkiyappan, R.: Stability results for Takagi-Sugeno fuzzy uncertain BAM neural networks with time delays in the leakage term. Neural Computing Appl. 22 (2013), S203-S219. | DOI
[18] Li, X., Song, S.: Impulsive control for existence, uniqueness and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays. IEEE Trans. Neural Networks Learning Systems 24 (2013), 868-877. | DOI
[19] Li, X., Song, S.: Stabilization of delay systems: Delay-dependent impulsive control. IEEE Trans. Automat. Control 62 (2017), 406-411. | DOI
[20] Li, H., Wang, C., Shi, P., Gao, H.: New passivity results for uncertain discrete-time stochastic neural networks with mixed time delays. Neurocomputing 73 (2010), 3291-3299. | DOI
[21] Li, X., Wu, J.: Stability of nonlinear differential systems with state-dependent delayed impulses. Automatica 64 (2016), 63-69. | DOI
[22] Li, Y., Yang, L., Sun, L.: Existence and exponential stability of an equilibrium point for fuzzy BAM neural networks with time-varying delays in leakage terms on time scales. Advances Diff. Equations 2013 (2013), 218. | DOI
[23] Liu, Y., D.Wang, Z., Liu, X. H.: Global exponential stability of generalized recurrent neural networks with discrete and distributed delays. Neural Networks 19 (2006), 5, 667-675. | DOI
[24] Mao, X.: Stochastic Differential Equations with their Applications. Horwood, Chichester 1997.
[25] Michel, A., Liu, D.: Qualitative Analysis and Synthesis of Recurrent Neural Networks. Marcel Dekker, New York 2002.
[26] Pan, L., Cao, J.: Robust stability for uncertain stochastic neural network with delay and impulses. Neurocomputing 94 (2012), 102-110. | DOI
[27] Raja, R., Raja, U. Karthik, Samidurai, R., Leelamani, A.: Dissipativity of discrete-time BAM stochastic neural networks with Markovian switching and impulses. J. Franklin Inst. 350 (2013), 3217-3247. | DOI | MR
[28] Raja, R., Raja, U. Karthik, Samidurai, R., Leelamani, A.: Passivity analysis for uncertain discrete time stochastic BAM neural networks with time-varying delays. Neural Computing Appl. 25 (2014), 751-766. | DOI
[29] Raja, R., Samidurai, R.: New delaydependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays. J. Franklin Inst. 349 (2012), 2108-2123. | DOI | MR
[30] Rubio, J.: Interpolation neural network model of a manufactured wind turbine. Neural Computing Appl. 28 (2017), 2017-2028. | DOI
[31] Song, Q., Cao, J.: Passivity of uncertain neural networks with both leakage delay and time-varying delay. Nonlinear Dynamics 67 (2012), 1695-1707. | DOI | MR
[32] Song, Q., Cao, J.: Passivity of uncertain neural networks with both leakage delay and time-varying delay. Nonlinear Dynamics 67 (2012), 1695-1707. | DOI | MR
[33] Tu, Z., Cao, J., Alsaedi, A., Hayat, T: Global dissipativity analysis for delayed quaternion-valued neural networks. Neural Networks 89 (2017), 97-104. | DOI
[34] Wu, Z., Park, Ju H., Su, H., Chu, J.: New results on exponential passivity of neural networks with time-varying delays. Nonlinear Analysis: Real World Appl. 13 (2012), 1593-1599. | DOI | MR
[35] Yang, C., Huang, T.: Improved stability criteria for a class of neural networks with variable delays and impulsive perturbations. Appl. Math. Comput. 243 (2014), 923-935. | DOI
[36] Zhao, Z., Song, Q., He, S.: Passivity analysis of stochastic neural networks with time-varying delays and leakage delay. Neurocomputing 47 (2015), 1-10. | DOI
[37] Zheng, C., Gong, C., Wang, Z.: New passivity conditions with fewer slack variables for uncertain neural networks with mixed delays. Neurocomputing 118 (2013), 237-244. | DOI
Cité par Sources :