nach oben

Journal of Applied Mathematics and Computing

Erschienen in:

01.10.2016 | Original Research

\(H_{\infty }\) filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays

verfasst von: Yajun Li, Wenping Xiao, Jingzhao Li, Like Jiao

Erschienen in: Journal of Applied Mathematics and Computing | Ausgabe 1-2/2016

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The \(H_{\infty }\) filter problem for a class of fuzzy stochastic discrete neural networks system with mixed delays is studied in this paper. The mixed delays consist of discrete and distributed delays. Based on discrete inequality technic and the Lyapunov–Krasovskii functional approach, sufficient conditions for the existence of admissible filters are established in terms of linear matrix inequalities, which ensure the asymptotical mean-square stability as well as a prescribed \(H_{\infty }\) disturbance attenuation level. Examples and simulations are provided to illustrate the effectiveness of the proposed method.

Nächster Artikel Dynamics of master equations with time-recurrent structure

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Zhao, Y., Gao, H., Lam, J., Du, B.: Stability and stabilization of delayed T-S fuzzy systems: a delay partitioning approach. IEEE Trans. Fuzzy Syst. 17, 750–762 (2009)CrossRef

Arunkumar, A., Sakthivel, R., Mathiyalagan, K., Park, J.H.: Robust stochastic stability of discrete-time fuzzy Markovian jump neural networks. ISA Trans. 53, 1006–1014 (2014)CrossRef

Wu, L.G., Su, X.J., Shi, P., Qiu, J.B.: A new approach to stability analysis and stabilization of discrete-time T-S fuzzy time-varying delay systems. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 41, 273–286 (2011)CrossRef

Yang, H.J., Shi, P., Li, X., Li, Z.W.: Fault-tolerant control for a class of T-S fuzzy systems via delta operator approach. Signal Process. 98, 166–173 (2014)CrossRef

Tang, Y., Fang, J., Xia, M., Gu, X.: Synchronization of Takagi-Sugeno fuzzy stochastic discrete-time complex networks with mixed time-varying delays. Appl. Math. Model. 34, 843–855 (2010)MathSciNetCrossRefMATH

Qiu, J., Feng, G., Gao, H.: Nonsynchronized-state estimation of multichannel networked nonlinear systems with multiple packet dropouts via T-S fuzzy affine dynamic models. IEEE Trans. Fuzzy Syst. 19, 75–90 (2011)CrossRef

Su, X., Shi, P., Wu, L., Nguang, S.K.: Induced \(l_{2}\) filtering of fuzzy stochastic systems with time-varying delays. IEEE Trans. Syst. Man Cybern. B 99, 1–14 (2012)

Wu, Z.G., Shi, P., Su, H.Y., Chu, J.: Reliable\( H_{\infty }\) control for discrete-time fuzzy systems with infinite-distributed delay. IEEE Trans. Fuzzy Syst. 20, 22–31 (2012)CrossRef

Li, J.P., Cao, J.D.: Robust stability for uncertain stochastic neural network with delay and impulses. Neurocomputing 94, 102–110 (2012)CrossRef

10.

Chen, W., Zheng, W.: Robust stability analysis for stochastic neural networks with time-varying delay. IEEE Trans. Neural Netw. 21, 508–514 (2010)CrossRef

11.

Xia, J.W., Park, J.H., Zeng, H.B., Shen, H.: Delay-difference-dependent robust exponential stability for uncertain stochastic neural networks with multiple delays. Neurocomputing 140, 210–218 (2014)CrossRef

12.

Raja, R., Samidurai, R.: New delay dependent robust asymptotic stability for uncertain stochastic recurrent neural networks with multiple time varying delays. J. Frankl. Inst. 349, 2108–2123 (2012)MathSciNetCrossRefMATH

13.

Li, X.D.: Global robust stability for stochastic interval neural networks with continuously distributed delays of neutral type. Appl. Math. Comput. 215, 4370–4384 (2010)MathSciNetCrossRefMATH

14.

Deng, F.Q., Hua, M.G., Liu, X.Z., Peng, Y.J., Fei, J.T.: Robust delay-dependent exponential stability for uncertain stochastic neural networks with mixed delays. Neurocomputing 74, 1503–1509 (2011)CrossRef

15.

Li, J.N., Li, L.S.: Mean-square exponential stability for stochastic discrete-time recurrent neural networks with mixed time delays. Neurocomputing 151, 790–797 (2015)CrossRef

16.

Ou, Y., Liu, H., Si, Y., Feng, Z.: Stability analysis of discrete-time stochastic neural networks with time-varying delays. Neurocomputing 73, 740–748 (2010)CrossRef

17.

Tang, Y., Fang, J., Xia, M., Yu, D.: Delay-distribution-dependent stability of stochastic discrete-time neural networks with randomly mixed time-varying delays. Neurocomputing 72, 3830–3838 (2009)CrossRef

18.

Li, T., Song, A.G., Fei, S.M.: Novel stability criteria on discrete-time neural networks with time-varying and distributed delays. Int. J. Neural Syst. 19, 269–283 (2009)CrossRef

19.

Arunkumar, A., Sakthivel, R., Mathiyalagan, K., Park, J.H.: Robust stochastic stability of discrete-time fuzzy Markovian jump neural networks. ISA Trans. 53, 1006–1014 (2014)CrossRef

20.

Song, Q.K., Liang, J.L., Wang, Z.D.: Passivity analysis of discrete-time neural networks with time-varying delay. Neurocomputing 72, 1782–1788 (2009)CrossRef

21.

Tang, Z., Park, Ju H., Lee, Tae H., Feng, J. W.: Mean square exponential synchronization for impulsive coupled neural networks with time-varying delays and stochastic disturbances. Complexity (2015). doi:10.1002/cplx.21647

22.

Mathiyalagan, K., Su, H.Y., Shi, P.: Exponential \(H_{\infty }\) filtering for discrete-time switched neural networks with random delays. IEEE Trans. Cybern. 45, 676–687 (2015)CrossRef

23.

Mathiyalagan, K., Park, J.H., Sakthivel, R., MarshalAnthoni, S.: Robust mixed \(H_{\infty }\) and passive filtering for networked Markov jump systems with impulses. IET Signal Process. 8, 809–822 (2014)CrossRef

24.

Hua, M., Tan, H., Chen, J., Fei, J.: Robust delay-range-dependent non-fragile \(H_{\infty }\) filtering for uncertain neutral stochastic systems with Markovian switching and mode-dependent time delays. J. Frankl. Inst. 352, 1318–1341 (2015)MathSciNetCrossRefMATH

25.

Zhang, L.X., Dong, X.K., Qiu, J.B., Alsaedi, A., Hayat, T.: \(H_\infty \) filtering for a class of discrete-time switched fuzzy systems. Nonlinear Anal.: Hybrid Syst. 14, 74–85 (2014)MathSciNetMATH

26.

Guo, X., Yang, G.: Reliable \(H_{\infty }\) filter design for discrete-time systems with sector-bounded nonlinearities: an LMI optimization approach. Acta Autom. Sin. 35, 1347–1351 (2009)MathSciNet

27.

Zhou, B., Zheng, W., Fu, Y., Duan, G.: \(H_{\infty }\) filtering for linear continuous-time systems subject to sensor nonlinearities. IET Control Theory Appl. 5, 1925–1937 (2011)MathSciNetCrossRef

28.

Zhuang, G.M.: Robust \( H_{\infty }\) filter design for uncertain stochastic Markovian jump Hopfield neural networks with mode-dependent time-varying delays. Neurocomputing 127, 181–189 (2014)CrossRef

29.

Li, X.J., Yang, G.H.: Switched-type \( H_{\infty }\) filter design for T-S fuzzy systems with unknown or partially unknown membership functions. IEEE Trans. Fuzzy Syst. 21, 385–392 (2013)CrossRef

30.

Qiu, J., Feng, G., Yang, J.: A new design of delay-dependent robust \(H_{\infty }\) filtering for discrete-time T-S fuzzy systems with time-varying delay. IEEE Trans. Fuzzy Syst. 17, 1044–1058 (2009)CrossRef

31.

Zhao, Y., Gao, H., Lam, J.: New results on filtering \(H_\infty \) for fuzzy systems with interval time-varying delays. Inf. Sci. 182, 2356–2369 (2011)MathSciNetCrossRefMATH

32.

Chang, X.H.: Robust nonfragile \(H_\infty \) filtering of fuzzy systems with linear fractional parametric uncertainties. IEEE Trans. Fuzzy Syst. 20, 1001–1011 (2012)CrossRef

33.

Zhang, H., Zhong, H., Dang, C.: Delay-dependent decentralized \(H_\infty \) filtering for discrete-time nonlinear interconnected systems with time-varying delay based on the T-S fuzzy model. IEEE Trans. Fuzzy Syst. 20, 431–443 (2012)CrossRef

34.

Wu, Y.Q., Su, H.Y., Wu, Z.G.: \(H_{\infty } \) filtering for discrete fuzzy stochastic systems with randomly occurred sensor nonlinearities. Signal Process. 108, 288–296 (2015)CrossRef

35.

Hua, M., Cai, Y., Ni, J., Fei, J.: Delay-dependent \(H_\infty \) filtering for discrete-time fuzzy stochastic systems with mixed delays and sector-bounded nonlinearity. J. Frankl. Inst. (2014). doi:10.1016/J.FranklinInst.11.009

36.

Chuang, L.Y., lien, C.H., Yu, K.W., Chen, J.D.: Robust \(H_{\infty } \) filtering for discrete switched systems with interval time-varying delay. Signal Process. 94, 661–669 (2014)CrossRef

37.

JarinaBanu, L., Balasubramaniam, P., Ratnavelu, K.: Robust stability analysis for discrete-time uncertain neural networks with leakage time-varying delay. Neurocomputing 151, 808–816 (2015)CrossRef

38.

Wu, L., Ho, D.W.C.: Fuzzy filter design for stochastic systems with application to sensor fault detection. IEEE Trans. Fuzzy Syst. 17, 233–242 (2009)CrossRef

39.

Wu, L., Wang, Z.: Fuzzy filtering of nonlinear fuzzy stochastic systems with time-varying delay. Signal Process. 89, 1739–1753 (2009)CrossRefMATH

40.

He, Y., Liu, G.P., Rees, D., Wu, M.: \(H_{\infty } \) filtering for discrete-time systems with time-varying delay. Signal Process. 89, 275–282 (2009)CrossRefMATH

41.

Li, Y.J.: \(H_{\infty } \) Filtering for discrete-time neural networks system with time- varying delay and sensor nonlinearities. Open Autom. Control Syst. J. 6, 165–174 (2014)MathSciNetCrossRef

Titel: filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays
verfasst von: Yajun Li
Wenping Xiao
Jingzhao Li
Like Jiao
Publikationsdatum: 01.10.2016
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Applied Mathematics and Computing / Ausgabe 1-2/2016
Print ISSN: 1598-5865
Elektronische ISSN: 1865-2085
DOI: https://doi.org/10.1007/s12190-015-0926-2

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 1-2/2016

Further spectral properties of the matrix with positive definite C and Hermitian B applied to wider classes of matrices C and B

Dynamics of master equations with time-recurrent structure

An active-set Levenberg–Marquardt method for degenerate nonlinear complementarity problem under local error bound conditions

Sufficiency and duality for nonsmooth multiobjective fractional programming problems involving -V-invexity

The existence of solutions for boundary value problems of fractional differential inclusion with a parameter

Erratum to: Convergence analysis of Legendre spectral projection methods for Hammerstein integral equations of mixed type