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L 2 - L ∞ state estimation of the high-order inertial neural network with time-varying delay: : Non-reduced order strategy

Authors:

Xiaona YangAuthors Info & Claims

Volume 607, Issue C

Pages 62 - 78

https://doi.org/10.1016/j.ins.2022.05.103

Published: 01 August 2022 Publication History

Abstract

As a first attempt, the L 2 - L ∞ state estimation issue of the high-order inertial neural networks with time-varying delay is put forward in this paper. A more direct method, non-reduced order method, is adopted here rather than reducing the order of the original second-order dynamics via substitution of variables. Our main objective is to construct an appropriate state estimator, which can not only guarantee the global h-stability of the undisturbed error dynamics, but also ensure the peak value of the estimation error is kept within a certain range. Through utilizing Lyapunov theory and some simple matrix calculation, a delay-dependent criterion is presented to elaborate the state estimator conforms to the expectation. In the end, an illustrative simulation example shows the correctness of the estimation technique proposed in this paper.

References

[1]

K.L. Babcock, R.M. Westervelt, Stability and dynamics of simple electronic neural networks with added inertia, Physica D 23 (1–3) (1986) 464–469.

Digital Library

[2]

J. Shi, Z. Zeng, Global exponential stabilization and lag synchronization control of inertial neural networks with time delays, Neural Networks 126 (2020) 11–20.

[3]

S. Lakshmanan, M. Prakash, C.P. Lim, R. Rakkiyappan, P. Balasubramaniam, S. Nahavandi, Synchronization of an inertial neural network with time-varying delays and its application to secure communication, IEEE Trans. Neural Networks Learn. Syst. 29 (1) (2018) 195–207.

[4]

X. Song, J. Man, C.K. Ahn, S. Song, Finite-time dissipative synchronization for Markovian jump generalized inertial neural networks with reaction-diffusion terms, IEEE Trans. Syst. Man Cybern.: Syst. 51 (6) (2021) 3650–3661.

[5]

X. Song, J. Man, J.H. Park, S. Song, Finite-time synchronization of reaction-diffusion inertial memristive neural networks via gain-scheduled pinning control, IEEE Trans. Neural Networks Learn. Syst. (2021),.

[6]

L. Hua, H. Zhu, K. Shi, S. Zhong, Y. Tang, Y. Liu, Novel finite-time reliable control design for memristor-based inertial neural networks with mixed time-varying delays, IEEE Trans. Circuits Syst. I Regul. Pap. 68 (4) (2021) 1599–1609.

[7]

L. Hua, S. Zhong, K. Shi, X. Zhang, Further results on finite-time synchronization of delayed inertial memristive neural networks via a novel analysis method, Neural Networks 127 (2020) 47–57.

[8]

J. Wang, T. Ru, H. Shen, J. Cao, J.H. Park, Finite-time L-2-L-infinity synchronization for semi-Markov jump inertial neural networks using sampled data, IEEE Trans. Network Sci. Eng. 8 (1) (2021) 163–173.

[9]

S. Qin, L. Gu, X. Pan, Exponential stability of periodic solution for a memristor-based inertial neural network with time delays, Neural Comput. Appl. 32 (8) (2020) 3265–3281.

[10]

X. Li, X. Li, C. Hu, Some new results on stability and synchronization for delayed inertial neural networks based on non-reduced order method, Neural Networks 96 (2017) 91–100.

Digital Library

[11]

J. Yu, C. Hu, H. Jiang, L. Wang, Exponential and adaptive synchronization of inertial complex-valued neural networks: A non-reduced order and non-separation approach, Neural Networks 124 (2020) 50–59.

Digital Library

[12]

J. Wang, X. Hu, J. Cao, J.H. Park, H. Shen, H ∞ state estimation for switched inertial neural networks with time-varying delays: A persistent dwell-time scheme, IEEE Trans. Syst. Man Cybern.: Syst. (2021),.

[13]

H. Wei, B. Wu, Z. Tu, Exponential synchronization and state estimation of inertial quaternion-valued Cohen-Grossberg neural networks: Lexicographical order method, Int. J. Robust Nonlinear Control 30 (6) (2020) 2171–2185.

[14]

H. Liu, Z. Wang, W. Fei, J. Li, F.E. Alsaadi, On finite-horizon H ∞ state estimation for discrete-time delayed memristive neural networks under stochastic communication protocol, Inf. Sci. 555 (2021) 280–292.

[15]

S. Liu, Z. Wang, B. Shen, G. Wei, Partial-neurons-based state estimation for delayed neural networks with state-dependent noises under redundant channels, Inf. Sci. 547 (2021) 931–944.

[16]

J. Hu, G.-P. Liu, H. Zhang, H. Liu, On state estimation for nonlinear dynamical networks with random sensor delays and coupling strength under event-based communication mechanism, Inf. Sci. 511 (2020) 265–283.

[17]

J. Cao, R. Manivannan, K.T. Chong, X. Lv, Enhanced L 2 - L ∞ state estimation design for delayed neural networks including leakage term via quadratic-type generalized free-matrix-based integral inequality, J. Franklin Inst. 356 (13) (2019) 7371–7392.

[18]

W. Qian, Y. Chen, Y. Liu, F.E. Alsaadi, Further results on L 2 - L ∞ state estimation of delayed neural networks, Neurocomputing 273 (2018) 509–515.

[19]

H. Wang, R. Dong, A. Xue, Y. Peng, Event-triggered L 2 - L ∞ state estimation for discrete-time neural networks with sensor saturations and data quantization, J. Franklin Inst. 356 (17) (2019) 10216–10240.

[20]

H. Liu, Z. Wang, W. Fei, J. Li, H ∞ and L 2 - L ∞ state estimation for delayed memristive neural networks on finite horizon: The Round-Robin protocol, Neural Networks 132 (2020) 121–130.

[21]

M. Pinto, Perturbations of asymptotically stable differential systems 4 (1–2) (1984) 161–176.

[22]

B.B. Nasser, K. Boukerrioua, M. Defoort, M. Djemai, M. Hammami, T. Lalegkirati, Sufficient conditions for uniform exponential stability and h-stability of some classes of dynamic equations on arbitrary time scales, Nonlinear Anal.: Hybrid Syst. 32 (2019) 54–64.

[23]

J. Wang, X. Wang, Y. Wang, X. Zhang, Non-reduced order method to global h-stability criteria for proportional delay high-order inertial neural networks, Appl. Math. Comput. 407 (2021) Article No. 126308.

[24]

Z. Dong, X. Wang, X. Zhang, A nonsingular M-matrix-based global exponential stability analysis of higher-order delayed discrete-time Cohen-Grossberg neural networks, Appl. Math. Comput. 385 (2020) Article No. 125401.

[25]

Z. Dong, X. Zhang, X. Wang, State estimation for discrete-time high-order neural networks with time-varying delays, Neurocomputing 411 (2020) 282–290.

[26]

X. Zhong, Y. Gao, Synchronization of inertial neural networks with time-varying delays via quantized sampled-data control, IEEE Trans. Neural Networks Learn. Syst. 32 (11) (2021) 4916–4930.

[27]

S. Chen, H. Jiang, B. Lu, Z. Yu, L. Li, Pinning bipartite synchronization for inertial coupled delayed neural networks with signed digraph via non-reduced order method, Neural Networks 129 (2020) 392–402.

[28]

Y. Tian, Z. Wang, Dissipative filtering for singular Markovian jump systems with generally hybrid transition rates, Appl. Math. Comput. 411 (2021) Article No. 126492.

[29]

Y. Tian, Z. Wang, A switched fuzzy filter approach to H ∞ filtering for Takagi-Sugeno fuzzy Markov jump systems with time delay: The continuous-time case, Inf. Sci. 557 (2021) 236–249.

[30]

G. Tan, Z. Wang, Z. Shi, Proportional-integral state estimator for quaternion-valued neural networks with time-varying delays, IEEE Trans. Neural Networks Learn. Syst. (2021),.

[31]

G. Tan, Z. Wang, Stability analysis of systems with time-varying delay via a delay-product-type integral inequality, Math. Methods Appl. Sci. (2022),.

[32]

J. Wang, C. Yang, J. Xia, Z.-G. Wu, H. Shen, Observer-based sliding mode control for networked fuzzy singularly perturbed systems under weighted try-once-discard protocol, IEEE Trans. Fuzzy Syst. (2021),.

[33]

H. Shen, X. Hu, J. Wang, J. Cao, W. Qian, Non-fragile H ∞ synchronization for Markov jump singularly perturbed coupled neural networks subject to double-layer switching regulation, IEEE Trans. Neural Networks Learn. Syst. (2021),.

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L 2 - L ∞ state estimation of the high-order inertial neural network with time-varying delay: Non-reduced order strategy

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Published In

cover image Information Sciences: an International Journal

Information Sciences: an International Journal Volume 607, Issue C

Aug 2022

1637 pages

ISSN:0020-0255

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Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 August 2022

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