Abstract
This paper investigates the problem of \(l_{2}\)–\(l_{\infty }\) asynchronous filtering for a class of discrete-time fuzzy neural networks subject to Markov jump parameters and unreliable communication links. Due to the fact that neural networks possess the nonlinear dynamic characteristic, it is difficult to deal with such a nonlinear characteristic directly, so the Takagi–Sugeno fuzzy model is introduced to approximate the system. Directed against the unreliable communication links, the data packet loss depicted by a stochastic variable with Bernoulli distribution and the signal quantization phenomenon occurring in communication channels are taken into consideration simultaneously. The attention of this paper is mainly centered on devising an asynchronous \(l_{2}\)–\(l_{\infty }\) filter for ensuring the \(l_{2}\)–\(l_{\infty }\) performance of the studied system under asynchronous conditions. Some sufficient conditions for the existence of the asynchronous \(l_{2}\)–\(l_{\infty }\) filter are presented. Finally, a numerical example is given to carry out the simulation experiment, which can verify the effectiveness of the obtained results.
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Arik S (2002) An analysis of global asymptotic stability of delayed cellular neural networks. IEEE Trans Neural Netw 13(5):1239–1242
Arik S, Tavsanoglu V (2000) On the global asymptotic stability of delayed cellular neural networks. IEEE Trans Circuits Syst I 47(4):571–574
Arik S (2019) A modified Lyapunov functional with application to stability of neutral-type neural networks with time delays. J Frankl Inst 356(1):276–291
Arik S (2020) New criteria for stability of neutral-type neural networks with multiple time delays. IEEE Trans Neural Netw Learn Syst 31(5):1504–1513
Balasubramaniam P, Vembarasan V, Rakkiyappan R (2011) Leakage delays in T–S fuzzy cellular neural networks. Neural Process Lett 33(2):111–136
Shen B, Wang Z, Liu X (2011) Bounded \(H_{\infty }\) synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon. IEEE Trans Neural Netw 22(1):145–157
Shen H, Huang Z, Cao J, Park JH (2020) Exponential \(H_{\infty }\) filtering for continuous-time switched neural networks under persistent dwell-time switching regularity. IEEE Trans Cybern 50(6):2440–2449
Shen H, Xing M, Wu Z, Cao J, Huang T (2020) \(l_{2}\)-\(l_{\infty }\) State estimation for persistent dwell-time switched coupled networks subject to round-robin protocol. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2995708
Hu J, Wang Z, Alsaadi FE, Hayat T (2017) Event-based filtering for time-varying nonlinear systems subject to multiple missing measurements with uncertain missing probabilities. Inform Fusion 38:74–83
Yang X, Cao J, Yang Z (2013) Synchronization of coupled reaction–diffusion neural networks with time-varying delays via pinning-impulsive controller. SIAM J Control Optim 51(5):3486–3510
Ru T, Xia J, Huang X, Cheng X, Wang J (2020) Reachable set estimation of delayed fuzzy inertial neural networks with Markov jumping parameters. J Frankl Inst 357(11):6882–6898
Xia Y, Wang J, Meng B, Chen X (2020) Further results on fuzzy sampled-data stabilization of chaotic nonlinear systems. Appl Math Comput 379:125225
Saravanakumar R, Ali MS, Ahn CK, Karimi HR, Shi P (2016) Stability of Markovian jump generalized neural networks with interval time-varying delays. IEEE Trans Neural Netw Learn Syst 28(8):1840–1850
Li J, Pan K, Zhang D, Su Q (2019) Robust fault detection and estimation observer design for switched systems. Nonlinear Anal Hybrid Syst 34:30–42
Su Q, Fan Z, Lu T, Long Y, Li J (2020) Fault detection for switched systems with all modes unstable based on interval observer. Inf Sci 517:167–182
Lin X, Zhang W, Yang Z, Zou Y (2020) Finite-time boundedness of switched systems with time-varying delays via sampled-data control. Int J Robust Nonlinear Control. https://doi.org/10.1002/rnc.4908
Lin X, Li X, Park JH (2020) Output-feedback stabilization for planar output-constrained switched nonlinear systems. Int J Robust Nonlinear Control. https://doi.org/10.1002/rnc.4850
Wang J, Yang C, Shen H, Cao J, Rutkowski L (2020) Sliding mode control for slow-sampling singularly perturbed systems subject to Markov jump parameters. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2020.2979860
Liu Y, Wang Z, Liu X (2012) State estimation for discrete-time neural networks with Markov-mode-dependent lower and upper bounds on the distributed delays. Neural Process Lett 36(1):1–19
Shen H, Chen M, Wu Z, Cao J, Park JH (2020) Reliable event-triggered asynchronous passive control for semi-Markov jump fuzzy systems and its application. IEEE Trans Fuzzy Syst 28(8):1708–1722
Wang X, Xia J, Wang J, Wang Z, Wang J (2020) Reachable set estimation for Markov jump LPV systems with time delays. Appl Math Comput 376:125117
Song X, Man J, Fu Z, Wang M, Lu J (2019) Memory-based state estimation of T–S fuzzy Markov jump delayed neural networks with reaction–diffusion terms. Neural Process Lett 50:2529–2546
Nagamani G, Radhika T (2016) Dissipativity and passivity analysis of Markovian jump neural networks with two additive time-varying delays. Neural Process Lett 44(2):571–592
Shen H, Jiao S, Huang T, Cao J (2019) An Improved result on sampled-data synchronization of Markov jump delayed neural networks. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2019.2931533
Xu S, Chen T, Lam J (2003) Robust \(H_{\infty }\) filtering for uncertain Markovian jump systems with mode-dependent time delays. IEEE Trans Automat Control 48(5):900–907
Dong H, Wang Z, Gao H (2012) Distributed \(H_{\infty }\) filtering for a class of Markovian jump nonlinear time-delay systems over lossy sensor networks. IEEE Trans Ind Electr 60(10):4665–4672
Zhang L, Zhu Y, Shi P, Zhao Y (2015) Resilient asynchronous \(H_{\infty }\) filtering for markov jump neural networks with unideal measurements and multiplicative noises. IEEE Trans Cybern 45(12):2840–2852
Choi HD, Ahn CK, Shi P, Lim MT, Song MK (2015) \(l_{2}\)-\(l_{\infty }\) filtering for Takagi–Sugeno fuzzy neural networks based on Wirtinger-type inequalities. Neurocomputing 153:117–125
Dong H, Wang Z, Ho DWC, Gao H (2010) Robust \(H_{\infty }\) fuzzy output-feedback control with multiple probabilistic delays and multiple missing measurements. IEEE Trans Fuzzy Syst 18(4):712–725
Shen H, Xing M, Wu Z-G, Xu S, Cao J (2019) Multi-objective fault-tolerant control for fuzzy switched systems with persistent dwell-time and its application in electric circuits. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2019.2935685
Rakkiyappan R, Sakthivel N, Park JH, Kwon OM (2013) Sampled-data state estimation for markovian jumping fuzzy cellular neural networks with mode-dependent probabilistic time-varying delays. Appl Math Comput 221:741–769
Balasubramaniam P, Vembarasan V, Rakkiyappan R (2012) Delay-dependent robust asymptotic state estimation of Takagi–Sugeno fuzzy Hopfield neural networks with mixed interval time-varying delays. Expert Syst Appl 39(1):472–481
Tong D, Zhu Q, Zhou W, Xu Y et al (2013) Adaptive synchronization for stochastic T–S fuzzy neural networks with time-delay and Markovian jumping parameters. Neurocomputing 117:91–97
Wu Z-G, Shi P, Su HY, Chu J (2014) Asynchronous \(l_{2}\)-\(l_{\infty }\) filtering for discrete-time stochastic Markov jump systems with randomly occurred sensor nonlinearities. Automatica 50(1):180–186
Yang R, Shi P, Liu G, Gao H (2011) Network-based feedback control for systems with mixed delays based on quantization and dropout compensation. Automatica 47(12):2805–2809
Fu M, Xie L (2005) The sector bound approach to quantized feedback control. IEEE Trans Automat Control 50(11):1698–1711
Li F, Xu S, Zhang B (2018) Resilient asynchronous \(H_{\infty }\) control for discrete-time Markov jump singularly perturbed systems based on hidden Markov model. IEEE Trans Syst Man Cybern Syst. https://doi.org/10.1109/TSMC.2018.2837888
Li F, Xu S, Shen H, Ma Q (2019) Passivity-based control for hidden Markov jump systems with singular perturbations and partially unknown probabilities. IEEE Trans Autom Control. https://doi.org/10.1109/TAC.2019.2953461
Wang Z, Liu Y, Liu X (2009) State estimation for jumping recurrent neural networks with discrete and distributed delays. Neural Netw 22(1):41–48
Shen H, Li F, Cao J, Wu Z-G, Lu G (2019) Fuzzy-model-based output feedback reliable control for network-based semi-Markov jump nonlinear systems subject to redundant channels. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2019.2959908
Wu Z-G, Park JH, Su H, Song B, Chu J (2012) Reliable \(H_{\infty }\) filtering for discrete-time singular systems with randomly occurring delays and sensor failures. IET Control Theory Appl 6(14):2308–2317
Zhang HY, Qiu ZP, Cao JD, Aty MA, Xiong LL (2019) Event-triggered synchronization for neutral-type semi-Markovian neural networks with partial mode-dependent time-varying delays. IEEE Trans Neural Netw and Learn Syst. https://doi.org/10.1109/TNNLS.2019.2955287
Zhang HY, Qiu ZP, Xiong LL (2019) Stochastic stability criterion of neutral-type neural networks with additive time-varying delay and uncertain semi-Markov jump. Neurocomputing 333:395–406
Zhang HY, Qiu ZP, Liu XZ, Xiong LL (2020) Stochastic robust finite-time boundedness for semi-Markov jump uncertain neutral-type neural networks with mixed time-varying delays via a generalized reciprocally convex combination inequality. Int J Robust Nonlinear Control 30(5):2001–2019
Nam PT, Trinh H, Pathirana PN (2015) Discrete inequalities based on multiple auxiliary functions and their applications to stability analysis of time-delay systems. J Frankl Inst 352(12):5810–5831
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This work was supported by the National Natural Science Foundation of China under Grants 61703004, the Natural Science Foundation of Anhui Province under Grant 1708085MF165.
Appendix
Appendix
1.1 Proof of Theorem 1
Proof
Consider the following Lyapunov-Krasovskii function for system \(\left( \breve{\Sigma }\right) \) as follows:
where
Calculating the value of \(E\left\{ \Delta V\left( p\right) \right\} ,\) we have
Furthermore, according to Remark 3 in [45] and combining (13), (14). Then, one can find that
where
Via operating iterative computations, it is referred from (21) that
under zero initial conditions, it can be obtained that
By using Schur complement to (16), it can see that
then, from (22)-(24), it can be derived that the following inequality holds for any non-zero \(\omega (p)\in l_{2}[0,\infty )\),
Clearly,the condition of Definition 1 can be guaranteed under the zero-initial conditions for any \(\omega \left( p\right) \in \left( 0,\infty \right] \). This completes the proof.
1.2 Proof of Theorem 2
Proof
Pre- and post-multiplying (17) by
and its transpose, then by using Schur complement, we have \(\Lambda _{hu}<0.\) Obviously, it can be noted that from (17) that the condition (15) is ensured. This completes the proof.
1.3 Proof of Theorem 3
Proof
From \(\left( J\tilde{P}_{hu}J^{T}-J\check{Z}_{hu}\right) \tilde{P}_{hu}^{-1}\left( J\tilde{P}_{hu}J^{T}-J\check{Z}_{hu}\right) ^{T}\ge 0,\) the condition holds as below for each \(h\in \mathbf {R},u\in \mathbf {S}\),
where
Then let \(\check{A}_{e,ju}=Z_{u}A_{e,ju}\), \(\check{B}_{e,ju}=Z_{u}B_{e,ju}\), \(\check{E}_{e,ju}=Z_{u}E_{e,ju}\), drawing support from Schur complement, Lemma 1, Lemma 2 as well as (25), from (18), it is clear that (17) is ensured. This completes the proof.
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Zhang, Y., Xia, J., Huang, X. et al. Asynchronous \(l_{2}\)–\(l_{\infty }\) Filtering for Discrete-Time Fuzzy Markov Jump Neural Networks with Unreliable Communication Links. Neural Process Lett 52, 2069–2088 (2020). https://doi.org/10.1007/s11063-020-10337-1
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DOI: https://doi.org/10.1007/s11063-020-10337-1