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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2017

10-05-2015 | Original Article

State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays

Authors: Mingang Hua, Huasheng Tan, Juntao Fei

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2017

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Abstract

The state estimation problem is considered for a class of discrete-time stochastic neural networks with Markovian jumping parameters in this paper. Norm-bounded parameter uncertainties in the state and measurement equation and time-varying delays are investigated. The neuron activation function satisfies sector-bounded conditions, and the nonlinear perturbation of the measurement equation satisfies standard Lipschitz condition and sector-bounded conditions. By constructing proper Lyapunov–Krasovskii functional, delay-dependent conditions are developed in terms of linear matrix inequalities (LMIs) to estimate the neuron state such that the dynamic of the estimation error system is asymptotically stable. Finally, numerical examples are shown to demonstrate the effectiveness and applicability of the proposed design method.
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Literature
1.
Balasubramaniam P, Kalpana M, Rakkiyappan R (2011) State estimation for fuzzy cellular neural networks with time delay in the leakage term, discrete and unbounded distributed delays. Comput Math Appl 62:3959–3972MathSciNetCrossRefMATH
2.
Balasubramaniam P, Lakshmanan S, Theesar S (2010) State estimation for Markovian jumping recurrent neural networks with interval time-varying delays. Nonlinear Dyn 60:661–675MathSciNetCrossRefMATH
3.
Balasubramaniam P, Rakkiyappan R (2008) Global asymptotic stability of stochastic recurrent neural networks with multiple discrete delays and unbounded distributed delays. Appl Math Comput 204:680–686MathSciNetMATH
4.
Bao H, Cao J (2011) Delay-distribution-dependent state estimation for discrete-time stochastic neural works with random delay. Neural Netw 24:19–28CrossRefMATH
5.
Chen B, Yu L, Zhang W (2011) \(H_\infty \) filtering for Markovian switching genetic regulatory networks with time-delays and stochastic disturbances. Circ Syst Signal Process 30:1231–1252MathSciNetCrossRefMATH
6.
Chen Y, Zheng W (2012) Stochastic state estimation for neural networks with distributed delays and Markovian jump. Neural Netw 25:14–20CrossRefMATH
7.
Chu H, Gao L (2009) State estimation for discrete-time Markov jumping stochastic neural networks with mixed time-delays. In: Proceedings of the Pacific-Asia Conference on Circuits, Communications and System. Chengdu, China, pp 717–721
8.
Eddahech A, Briat O, Ayadi M, Vinassa J (2014) Modeling and adaptive control for supercapacitor in automotive applications based on artificial neural networks. Electr Power Syst Res 106:134–141CrossRef
9.
He Q, Liu D, Wu H, Ding S (2014) Robust exponential stability analysis for interval Cohen-Grossberg type BAM neural networks with mixed time delays. Int J Mach Learn Cybern 5:23–38CrossRef
10.
He Y, Wang Q, Wu M, Lin C (2006) Delay-dependent state estimation for delayed neural networks. IEEE Trans Neural Netw 17:1077–1081CrossRef
11.
Hua M, Liu X, Deng F, Fei J (2010) New results on robust exponential stability of uncertain stochastic neural networks with mixed time-varying delays. Neural Process lett 32:219–233CrossRef
12.
Huang H, Feng G (2009) Delay-dependent \(H_\infty \) and generalized \(H_2\) filtering for delayed neural networks. IEEE Trans Circuits I 56:846–857MathSciNetCrossRef
13.
Huang H, Feng G (2011) State estimation of recurrent neural networks with time-varying delay: a novel delay partition approach. Neurocomputing 74:792–796CrossRef
14.
Huang H, Feng G, Cao J (2008) Robust state estimation for uncertain neural networks with time-varying delay. IEEE Trans Neural Netw 19:1329–1339CrossRef
15.
Huang H, Feng G, Cao J (2010) State estimation for static neural networks with time-varying delay. Neural Netw 23:1202–1207CrossRef
16.
17.
Kwon O, Park M, Park J, Lee S, Cha E (2013) New criteria on delay-dependent stability for discrete-time neural networks with time-varying delays. Neurocomputing 121:185–194CrossRefMATH
18.
Li H, Chen B, Zhou Q, Qian W (2009) Robust stability for uncertain delayed fuzzy Hopfield neural networks with Markovian jumping parameters. IEEE Trans Syst Man Cybern B 39:94–102CrossRef
19.
Li T, Fei S (2007) Exponential state estimation for recurrent neural networks with distributed delays. Neurocomputing 71:428–438CrossRef
20.
Li H, Jing X, Karimi H (2014) Output-feedback-based \(H_\infty \) control for vehicle suspension systems with control delay. IEEE Trans Ind Electron 61:436–446CrossRef
21.
Li H, Liu H, Gao H, Shi P (2012) Reliable fuzzy control for active suspension systems with actuator delay and fault. IEEE Trans Fuzzy Syst 20:342–357CrossRef
22.
Liang J, Lam J, Wang Z (2009) State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates. Phys Letts A 373:4328–4337MathSciNetCrossRefMATH
23.
Liang J, Wang Z, Liu X (2009) State estimation for coupled uncertain stochastic networks with missing measurements and time-varying delays: The discrete-time case. IEEE Trans Neural Netw 20:781–793CrossRef
24.
Liao C, Lu C, Zheng K, Ting C (2009) A delay-dependent approach to design state estimator for discrete stochastic recurrent neural network with interval time-varying delays. ICIC Express Lett 3:465–470
25.
Liu Y, Wang Z, Liu X (2007) Design of exponential state estimators for neural networks with mixed time delays. Phys Letts A 364:401–412CrossRef
26.
Liu Y, Wang Z, Liu X (2008) Robust \(H_\infty \) filtering for discrete nonlinear stochastic systems with time-varying delay. J Math Anal Appl 341:318–336MathSciNetCrossRefMATH
27.
Lu C (2008) A delay-range-dependent approach to design state estimation for discrete-time recurrent neural networks with interval time-varying delay. IEEE Trans Circuits II 55:1163–1167
28.
29.
Mohammadian M, Abolmasoumi A, Momeni H (2012) \(H_\infty \) mode-independent filter design for Markovian jump genetic regulatory networks with time-varying delays. Neurocomputing 87:10–18CrossRef
30.
Mou S, Gao H, Qiang W, Fei Z (2008) State estimation for discrete-time neural networks with time-varying delays. Neurocomputing 72:643–647CrossRef
31.
Ou Y, Shi P, Liu H (2010) A mode-dependent stability criterion for delayed discrete-time stochastic neural networks with Markovian jumping parameters. Neurocomputing 73:1491–1500CrossRef
32.
Park J, Kwon O (2009) Further results on state estimation for neural networks of neutral-type with time-varying delay. Appl Math Comput 208:65–75MathSciNetMATH
33.
Park J, Kwon O, Lee S (2008) State estimation for neural networks of neutral-type with interval time-varying delays. Appl Math Comput 203:217–223MathSciNetMATH
34.
Syed Ali M (2014) Robust stability of stochastic uncertain recurrent neural networks with Markovian jumping parameters and time-varying delays. Int J Mach Learn Cybern 5(1):13–22CrossRefMATH
35.
Syed Ali M (2014) Stability analysis of Markovian jumping stochastic Cohen-Grossberg neural networks with discrete and distributed time varying delays. Chin Phys B 23(6):060702CrossRef
36.
Syed Ali M (2015) Stability of Markovian jumping recurrent neural networks with discrete and distributed time-varying delays. Neurocomputing 149:1280–1285CrossRef
37.
Syed Ali M, Marudai M (2011) Stochastic stability of discrete-time uncertain recurrent neural networks with Markovian jumping and time-varying delay. Math Comput Model 54(9–10):1979–1988MathSciNetCrossRefMATH
38.
Wang T, Ding Y, Zhang L, Hao K (2013) Robust state estimation for discrete-time stochastic genetic regulatory networks with probabilistic measurement delays. Neurocomputing 111:1–12CrossRef
39.
Wang Z, Ho D, Liu X (2005) State estimation for delayed neural networks. IEEE Trans Neural Netw 16:279–284CrossRef
40.
Wang Z, Liu Y, Liu X (2009) State estimation for jumping recurrent neural networks with discrete and distributed delays. Neural Netw 22:41–48CrossRefMATH
41.
Wang Z, Liu Y, Liu X, Shi Y (2010) Robust state estimation for discrete-time stochastic neural networks with probabilistic measurement delays. Neurocomputing 74:256–264CrossRef
42.
Wang H, Song Q (2010) State estimation for neural networks with mixed interval time-varying delays. Neurocomputing 73:1281–1288CrossRef
43.
Wang W, Zhong S, Liu F (2012) Robust filtering of uncertain stochastic genetic regulatory networks with time-varying delays. Chaos Soliton Fract 45:915–929MathSciNetCrossRefMATH
44.
Wei G, Wang Z, Lam J, Fraser K, Rao G, Liu X (2009) Robust filtering for stochastic genetic regulatory networks with time-varying delay. Math Biosci 220:73–80MathSciNetCrossRefMATH
45.
Wu Z, Su H, Chu J (2010) State estimation for discrete Markovian jumping neural networks with time delay. Neurocomputing 73:2247–2254CrossRef
46.
Zhang C, Chen Y, Wang J (2012) A state estimator of stochastic delayed neural networks. In: Proceedings of 24th Chinese Control and Decision Conference. Taiyuan, China, pp 2829–2832
47.
Zhang F, Zhang Y (2013) State estimation of neural networks with both time-varying delays and norm-bounded parameter uncertainties via a delay decomposition approach. Commun Nonlinear Sci Numer Simul 18:3517–3529MathSciNetCrossRefMATH
48.
Zheng C, Zhang Y, Wang Z (2014) Stability analysis of stochastic reaction-diffusion neural networks with Markovian switching and time delays in the leakage terms. Int J Mach Learn Cybern 5:3–12CrossRef
Metadata
Title
State estimation for uncertain discrete-time stochastic neural networks with Markovian jump parameters and time-varying delays
Authors
Mingang Hua
Huasheng Tan
Juntao Fei
Publication date
10-05-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2017
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0373-2

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