This work mainly concentrates on the state estimation for Markov jump coupled neural networks (MJCNNs) with reaction-diffusion terms, in which the memory controller is employed. First, the considered MJCNNs model is introduced, and the dynamic error system can be obtained based on the proposed state estimator. Then, a memory controller that involves constant signal transmission delay is designed. Moreover, by Lyapunov functional method, inequality technique and Kronecker product law, a novel stable and extended dissipative analysis criteria can be established to ensure that the stability of the error system the error system. Meanwhile, the controller gains can be obtained by solving linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness of the developed method.
ChenZZhangBZhangY, et al. (2017) Dissipative fuzzy filtering for nonlinear networked systems with limited communication links. IEEE Transactions on Systems, Man, and Cybernetics: Systems50(3): 962–971.
2.
ChengJParkJHKarimiHR, et al. (2017) A flexible terminal approach to sampled-data exponentially synchronization of Markovian neural networks with time-varying delayed signals. IEEE Transactions on Cybernetics48(8): 2232–2244.
3.
DaiMXiaJXiaH, et al. (2019) Event-triggered passive synchronization for Markov jump neural networks subject to randomly occurring gain variations. Neurocomputing331: 403–411.
4.
DingKZhuQLiuL (2019) Extended dissipativity stabilization and synchronization of uncertain stochastic reaction-diffusion neural networks via intermittent non-fragile control. Journal of the Franklin Institute356(18): 11690–11715.
5.
FangWYaoXZhaoX, et al. (2016) A stochastic control approach to maximize profit on service provisioning for mobile cloudlet platforms. IEEE Transactions on Systems, Man, and Cybernetics: Systems48(4): 522–534.
6.
GuoTDongJLiH, et al. (2017) Simple convolutional neural network on image classification. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 721–724. Beijing, China.
7.
HanTJiangDZhaoQ, et al. (2018) Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control40(8): 2681–2693.
8.
HuXFengGDuanS, et al. (2016) A memristive multilayer cellular neural network with applications to image processing. IEEE Transactions on Neural Networks and Learning Systems28(8): 1889–1901.
9.
JiangBKarimiHYangS, et al. (2020) Observer-based adaptive sliding mode control for nonlinear stochastic Markov jump systems via T–S fuzzy modeling: Applications to robot arm model. IEEE Transactions on Industrial Electronics68(1): 466–477.
10.
JiangXMahadevanSYuanY (2017) Fuzzy stochastic neural network model for structural system identification. Mechanical Systems and Signal Processing82: 394–411.
11.
JiaoSShenHWeiY, et al. (2018) Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays. Applied Mathematics and Computation336: 338–350.
12.
KumarRSrivastavaSGuptaJ, et al. (2019) Comparative study of neural networks for dynamic nonlinear systems identification. Soft Computing23(1): 101–114.
13.
LiFSongSZhaoJ, et al. (2019) Synchronization control for Markov jump neural networks subject to HMM observation and partially known detection probabilities. Applied Mathematics and Computation360: 1–13.
14.
LiFZhengWXuS (2020) HMM-based filtering for Markov jump systems with partial information and sensor nonlinearities. International Journal of Robust and Nonlinear Control30(16): 6891–6908.
15.
LiuHWangZShenB, et al. (2017) Event-triggered state estimation for delayed stochastic memristive neural networks with missing measurements: The discrete time case. IEEE Transactions on Neural Networks and Learning Systems29(8): 3726–3737.
16.
LiuYZhaoZChangF, et al. (2020) An anchor-free convolutional neural network for real-time surgical tool detection in robot-assisted surgery. IEEE Access8: 78193–78201.
17.
LuJ (2008) Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Chaos, Solitons & Fractals35(1): 116–125.
18.
MaQXuSZouY, et al. (2012) Synchronization of stochastic chaotic neural networks with reaction-diffusion terms. Nonlinear Dynamics67(3): 2183–2196.
19.
ManivannanRCaoY (2018) Design of generalized dissipativity state estimator for static neural networks including state time delays and leakage delays. Journal of the Franklin Institute355(9): 3990–4014.
20.
ManivannanRSamiduraiRCaoJ, et al. (2017) Global exponential stability and dissipativity of generalized neural networks with time-varying delay signals. Neural Networks87: 149–159.
21.
MarbanASrinivasanVSamekW, et al. (2019) A recurrent convolutional neural network approach for sensorless force estimation in robotic surgery. Biomedical Signal Processing and Control50: 134–150.
22.
MuralitharanKSakthivelRVishnuvarthanR (2018) Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing273: 199–208.
23.
QuYXiongN (2012) RFH: A resilient, fault-tolerant and high-efficient replication algorithm for distributed cloud storage. In: 2012 41st International Conference on Parallel Processing, pp. 520–529. Pittsburgh, PA, USA.
24.
SamiduraiRManivannanRAhnC, et al. (2016) New criteria for stability of generalized neural networks including Markov jump parameters and additive time delays. IEEE Transactions on Systems, Man, and Cybernetics: Systems48(4): 485–499.
25.
SaravanakumarRRajchakitGAhnC, et al. (2017) Exponential stability, passivity, and dissipativity analysis of generalized neural networks with mixed time-varying delays. IEEE Transactions on Systems, Man, and Cybernetics: Systems49(2): 395–405.
26.
SelvarajPSakthivelRKwonO (2019) Finite-time synchronization of stochastic coupled neural networks subject to Markovian switching and input saturation. Neural Networks105: 154–165.
27.
ShanmugamLManiPRajanR, et al. (2020) Adaptive synchronization of reaction-diffusion neural networks and its application to secure communication. IEEE Transactions on Cybernetics50(3): 911–922.
28.
ShenHZhuYZhangL, et al. (2016) Extended dissipative state estimation for Markov jump neural networks with unreliable links. IEEE Transactions on Neural Networks and Learning Systems28(2): 346–358.
29.
ShenYWuZShiP, et al. (2018) Asynchronous filtering for Markov jump neural networks with quantized outputs. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 49(2): 433–443.
30.
ShengYZhangHZengZ (2017) Synchronization of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite delays. IEEE Transactions on Cybernetics47(10): 3005–3017.
31.
SongXManJAhnC, et al. (2019a) Finite-time dissipative synchronization for Markovian jump generalized inertial neural networks with reaction-diffusion terms. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 6(51): 3650–3661.
32.
SongXManJSongS, et al. (2020) Integral sliding mode synchronization control for Markovian jump inertial memristive neural networks with reaction-diffusion terms. Neurocomputing378: 324–334.
33.
SongXWangMSongS, et al. (2019b) Quantized output feedback control for nonlinear Markovian jump distributed parameter systems with unreliable communication links. Applied Mathematics and Computation353: 371–395.
34.
SunJWangXXiongN, et al. (2018) Learning sparse representation with variational auto-encoder for anomaly detection. IEEE Access6: 33353–33361.
35.
TaoJWuZSuH, et al. (2018) Asynchronous and resilient filtering for Markovian jump neural networks subject to extended dissipativity. IEEE Transactions on Cybernetics49(7): 2504–2513.
36.
TuZCaoJAlsaediA, et al. (2017) Global dissipativity analysis for delayed quaternion-valued neural networks. Neural Networks89: 97–104.
37.
WangJZhangXWuH, et al. (2018) Finite-time passivity and synchronization of coupled reaction-diffusion neural networks with multiple weights. IEEE Transactions on Cybernetics49(9): 3385–3397.
38.
WangLWangZWeiG, et al. (2017) Finite-time state estimation for recurrent delayed neural networks with component-based event-triggering protocol. IEEE Transactions on Neural Networks and Learning Systems29(4): 1046–1057.
39.
WeiHLiRChenC, et al. (2016) Extended dissipative analysis for memristive neural networks with two additive time-varying delay components. Neurocomputing216: 429–438.
40.
WeiTLinPWangY, et al. (2019) Stability of stochastic impulsive reaction-diffusion neural networks with S-type distributed delays and its application to image encryption. Neural Networks116: 35–45.
41.
WuHZhaoJ (2018) Deep convolutional neural network model based chemical process fault diagnosis. Computers & Chemical Engineering115: 185–197.
42.
WuZJiangBKaoY (2019a) Finite-time filtering for stochastic Markovian jump systems with distributed time-varying delays based on optimisation algorithm. IET Control Theory & Applications13(5): 702–710.
43.
WuZKarimiHDangC (2019b) An approximation algorithm for graph partitioning via deterministic annealing neural network. Neural Networks117: 191–200.
44.
WuZLiBGaoC, et al. (2019c) Observer-based control design for singular switching semi-Markovian jump systems with random sensor delays. ISA Transactions. S0019-0578(19)30417-3.
45.
XiaoJLiYZhongS, et al. (2016) Extended dissipative state estimation for Memristive neural networks with time-varying delay. ISA Transactions64: 113–128.
46.
XiaoJZhongS (2018) Extended dissipative conditions for memristive neural networks with multiple time delays. Applied Mathematics and Computation323: 145–163.
47.
XiaoQHuangTZengZ (2018) Passivity and passification of fuzzy memristive inertial neural networks on time scales. IEEE Transactions on Fuzzy Systems26(6): 3342–3355.
48.
XuNSunL (2019) Stability analysis of Markov jump neural networks with mixed delays in finite frequency domain based on PDC controller. IEEE Access7: 143282–143292.
49.
XuRKaoYGaoM (2017) Finite-time synchronization of Markovian jump complex networks with generally uncertain transition rates. IEEE Transactions on Cybernetics39(1): 52–60.
50.
XuYLuRShiP, et al. (2017) Robust estimation for neural networks with randomly occurring distributed delays and Markovian jump coupling. IEEE Transactions on Neural Networks and Learning Systems29(4): 845–855.
51.
YanZSongYParkJ (2017) Finite-time stability and stabilization for stochastic Markov jump systems with mode-dependent time delays. ISA Transactions68: 141–149.
52.
ZhangBXuSZouY (2008) Improved delay-dependent exponential stability criteria for discrete-time recurrent neural networks with time-varying delays. Neurocomputing 72(1–3): 321–330.
53.
ZhangXHanQ (2017) State estimation for static neural networks with time-varying delays based on an improved reciprocally convex inequality. IEEE Transactions on Neural Networks and Learning Systems29(4): 1376–1381.
54.
ZhangYLiuXJiangJ, et al. (2018) Dissipativity-based resilient asynchronous control for Markov jump systems with sector-bounded nonlinearities. Transactions of the Institute of Measurement and Control, 40(9): 2821–2830.
55.
ZhaoJParkJXuS (2020) Quasi-time-dependent asynchronous filtering for discrete time switched systems via the event triggering mechanism. International Journal of Robust and Nonlinear Control30(12): 4633–4651.
56.
ZhouXChengJCaoJ, et al. (2020) Asynchronous dissipative filtering for nonhomogeneous Markov switching neural networks with variable packet dropouts. Neural Networks130: 229–237.
57.
ZhouYXiaJShenH, et al. (2019) Extended dissipative learning of time-delay recurrent neural networks. Journal of the Franklin Institute356(15): 8745–8769.
