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## FINITE-TIME PASSIVITY OF DISCRETE-TIME T-S FUZZY NEURAL NETWORKS WITH TIME-VARYING DELAYS | ||

Iranian Journal of Fuzzy Systems | ||

مقاله 8، دوره 15، شماره 4، مهر و آبان 2018، صفحه 93-107
اصل مقاله (444 K)
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نوع مقاله: Research Paper | ||

شناسه دیجیتال (DOI): 10.22111/ijfs.2018.4117 | ||

نویسندگان | ||

M. Syed Ali^{*} ^{1}؛ K. Meenakshi^{1}؛ N. Gunasekaran^{2}؛ M. Usha^{3}
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^{1}Department of Mathematics,, Thiruvalluvar University,, Vellore632115, Tamil Nadu, India | ||

^{2}Department of Mathematics, Thiruvalluvar University, Vellore632115, Tamil Nadu, India | ||

^{3}Department of Mathematics, Thiruvalluvar University, Vellore-632115, Tamil Nadu, India | ||

چکیده | ||

This paper focuses on the problem of finite-time boundedness and finite-time passivity of discrete-time T-S fuzzy neural networks with time-varying delays. A suitable Lyapunov--Krasovskii functional(LKF) is established to derive sufficient condition for finite-time passivity of discrete-time T-S fuzzy neural networks. The dynamical system is transformed into a T-S fuzzy model with uncertain parameters. Furthermore, the obtained passivity criteria is established in terms of Linear matrix inequality (LMI), which can be easily checked by using the efficient MATLAB LMI toolbox. Finally, some numerical cases are given to illustrate the effectiveness of the proposed approach. | ||

کلیدواژهها | ||

Lyapunov-Krasovskii function؛ Discrete-time neural networks (DNNs)؛ Passivity؛ Finite-time stability؛ T-S fuzzy rule | ||

مراجع | ||

[1] C. K. Ahn, P. Shi, R. K. Agarwal and J. Xu, L1 performance of single and interconnected neural networks with time-varying delay, Information Sciences, 346 (2016), 412{423. [2] A. Arunkumar, R. Sakthivel, K. Mathiyalagan and S. Marshal Anthoni, Robust stability criteria for discrete-time switched neural networks with various activation functions, Applied Mathematics and Computation , 218 (2012), 10803{10816. [3] A. Arunkumar, R. Sakthivel, K. Mathiyalagan and J. H. Park, Robust stochastic stability of discrete-time fuzzy Markovian jump neural networks, ISA transactions, 53 (2014), 1006{ 1014. [4] J. Bai, R. Lu, A. Xue, Q. She and Z. Shi, Finite-time stability analysis of discrete-time fuzzy Hopfield neural network, Neurocomputing, 159 (2015), 263{267. [5] B. Boyd, L. Ghoui, E. Feron and V. Balakrishnan, Linear matrix inequalities in system and control theory, Philadephia, PA: SIAM. (1994). [6] J. Cao, R. Rakkiyappan, K. Maheswari and A. Chandrasekar, Exponential H1 filtering analy- sis for discrete-time switched neural networks with random delays using sojourn probabilities, Science China Technological Sciences, 59 (2016), 387{402. [7] Y. L. Chien, W. L. Chin and H. T. Hsun, Delay-range-dependent global robust passivity anal- ysis of discrete-time uncertain recurrent neural networks with interval time-varying delay, Discrete Dynamics in Nature and Society, (2009), 1{14. [8] P. Dorato, Short time stability in linear time-varying systems, Proc IRE Int Convention Record Part 4, (1961), 83{87. [9] K. Gu, An integral inequality in the stability problem of time-delay systems, in: Proc. 39th IEEE Conf. Decision and Control, Sydney, Australia, (2000), 2805-2810. [10] M. Gupta, L. Jin and N. Homma, Static and dynamic neural networks: from fundamentals to advanced theory, Wiley-IEEE Press, 2013. [11] M. Han, Y. N. Sun and Y. N. Fan, An improved fuzzy neural network based on T-S model, Expert Systems with Applications, 34 (2008), 2905{2920. [12] L. Jarina Banu and P. Balasubramaniam, Robust stability analysis for discrete-time neural networks with time-varying leakage delays and random parameter uncertainties, Neurocom- puting, 179 (2016), 126{134. [13] R. Li, J. Cao and Z. Tu, Passivity analysis of memristive neural networks with probabilistic time-varying delays, Neurocomputing, 191 (2016), 249{262. [14] Y. Liu, Z. Wang and X. Liu, Global exponential stability of generalized recurrent neural networks with discrete and distributed delays, Neural Networks, 19 (2006), 667{675. [15] H. Liu, Z. Wang, B. Shen and F. E. Alsaadi, state estimation for discrete-time memris- tive recurrent neural networks with stochastic time-delays, International Journal of General Systems, 45 (2016), 633{647. [16] X. G. Liu, F. X. Wang and Y. J. Shu, A novel summation inequality for stability analysis of discrete-time neural networks, Journal of Computational and Applied Mathematics, 304 (2016), 160{171. [17] A. Liu, L. Yun, D. Zhang and W. Zhang, Finite-time H1 control for discrete-time genetic regulatory networks with random delays and partly unknown transition probabilities, Journal of the Franklin Institute, 350 (2013), 1944{1961. [18] K. Mathiyalagan, J. H. Park and R. Sakthivel, Novel results on robust finite-time passivity for discrete-time delayed neural networks, Neurocomputing, 177 (2016), 585{593. [19] K. Mathiyalagan, H. Su, P. Shi and R. Sakthivel, Exponential H1 filtering for discrete-time switched neural networks with random delays, IEEE transactions on cybernetics, 45 (2015), 676{687. [20] G. Nagamani and S. Ramasamy, Dissipativity and passivity analysis for discrete-time T- S fuzzy stochastic neural networks with leakage time-varying delays based on Abel lemma approach, Journal of the Franklin Institute, 353 (2016), 3313{3342. [21] S. Ramasamy, G. Nagamani and Q. Zhu, Robust dissipativity and passivity analysis for discrete-time stochastic T{S fuzzy Cohen{Grossberg Markovian jump neural networks with mixed time delays, Nonlinear Dynamics, 85(4) (2016), 2777{2799. [22] R. Saravanakumar, M. Syed Ali, C. K. Ahn, H. R. Karimi and P. Shi, Stability of Markovian jump generalized neural networks with interval time-varying delays, IEEE transactions on neural networks and learning systems 28(8) (2017), 1840{1850. [23] P. Shi, Y. Zhang and R. K. Agarwal, Stochastic finite-time state estimation for discrete time-delay neural networks with Markovian jumps, Neurocomputing, 151 (2015), 168{174. [24] M. Syed Ali and M. Marudai, Stochastic stability of discrete-time uncertain recurrent neural networks with Markovian jumping and time-varying delays, Mathematical and Computer Modelling, 54 (2011), 1979-1988. [25] M. Syed Ali, N. Gunasekaran and Q. Zhu, State estimation of T-S fuzzy delayed neural networks with Markovian jumping parameters using sampled-data control, Fuzzy Sets and Systems, 306 (2017), 87-104. [26] M. Syed Ali, R. Saravanakumar and J. Cao, New passivity criteria for memristor-based neutral-type stochastic BAM neural networks with mixed time-varying delays, Neurocomput- ing, 171 (2016), 1533{1547. [27] T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE transactions on systems, man, and cybernetics, 15 (1985), 116{132. [28] H. Wang and Q. Zhu, Finite-time stabilization of high-order stochastic nonlinear systems in strict-feedback form, Automatica, 54 (2015), 284{291. [29] L. Wu, Z. Feng and J. Lam, Stability and synchronization of discrete-time neural networks with switching parameters and time-varying delays, IEEE transactions on neural networks and learning systems, 24 (2013), 1957{1972. [30] W. Xie and Q. Zhu, Mean square exponential stability of stochastic fuzzy delayed Cohen- Grossberg neural networks with expectations in the coefficients, Neurocomputing, 166 (2015), 133{139. [31] E. Yucel, M. Syed Ali, N. Gunasekaran and S. Arik, Sampled-data filtering of Takagi-Sugeno fuzzy neural networks with interval time-varying delays, Fuzzy Sets and Systems, 316 (2017), 69-81. [32] H. Zhang and J. Wang State estimation of discrete-time Takagi{Sugeno fuzzy systems in a network environment, IEEE Transactions on Cybernetics, 45 (2015), 1525{1536. [33] Y. Zhang, P. Shi, R. K. Agarwal and Y. Shi, Dissipativity analysis for discrete time-delay fuzzy neural networks with Markovian jumps, IEEE Transactions on Fuzzy Systems, 24 (2016), 432{443. [34] D. Zhang and L. Yu, Passivity analysis for discrete-time switched neural networks with various activation functions and mixed time delays, Nonlinear Dynamics, 67 (2012), 403{ 411. [35] Y. Zhang, P. Shi, S. K. Nguang, J. Zhang and H. R. Karimi, Finite-time boundedness for uncertain discrete neural networks with time-delays and Markovian jumps, Neurocomputing, 140 (2014), 1{7. [36] C. Zheng, J. Cao, M. Hu and X. Fan, Finite-time stabilisation for discrete-time T-S fuzzy model system with channel fading and two types of parametric uncertainty, International Journal of Systems Science, 48 (2017), 34{42. [37] Q. Zhu, R. Rakkiyappan and A. Chandrasekar, Stochastic stability of Markovian jump BAM neural networks with leakage delays and impulse control, Neurocomputing, 136 (2014), 136{ 151. [38] Q. Zhu and J. Cao, Mean-square exponential input-to-state stability of stochastic delayed neural networks, Neurocomputing, 131 (2014), 157{163. [39] Q. Zhu and J. Cao, Stability analysis of Markovian jump stochastic BAM neural networks with impulse control and mixed time delays, IEEE Transactions on Neural Networks and Learning Systems, 23 (2012), 467{479. [40] Q. Zhu and J. Cao, Stability of Markovian jump neural networks with impulse control and time varying delays, Nonlinear Analysis: Real World Applications, 13 (2012), 2259{2270. [41] Q. Zhu and J. Cao, Exponential stability of stochastic neural networks with both Markovian jump parameters and mixed time delays, IEEE Transactions on Systems, Man, and Cyber- netics, Part B (Cybernetics), 41 (2011), 341{353. [42] Q. Zhu, J. Cao and R. Rakkiyappan, Exponential input-to-state stability of stochastic Cohen- Grossberg neural networks with mixed delays, Nonlinear Dynamics, 79 (2015), 1085{1098. [43] Q. Zhu and X. Li, Exponential and almost sure exponential stability of stochastic fuzzy delayed Cohen-Grossberg neural networks, Fuzzy Sets and Systems, 203 (2012), 74{94. | ||

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