<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si108.svg"><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo linebreak="badbreak">-</mml:mo><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> state estimation of the high-order inertial neural network with time-varying delay: Non-reduced order strategy

Wang, Junlan; Zhang, Xian; Wang, Xin; Yang, Xiaona

doi:10.1016/j.ins.2022.05.103

Cited by 12 publications

(2 citation statements)

References 29 publications

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“…The method directly uses the generalized matrix inverses and the definitions of GES, and it avoids the construction of any LKF; (2) The obtained sufficient conditions are composed of linear scalars inequalities that is easy to solve; (3) It is suitable for the more general neural network models after a small modification. For example, memristor-based NNs [40], inertial neural works [41] and high-order NNs [4], [42], [43].…”

Section: Discussionmentioning

confidence: 99%

State Estimation for a Class of Discrete-Time BAM Neural Networks With Multiple Time-Varying Delays

Cong

Zhang

2023

IEEE Access

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For a class of discrete-time bidirectional associative memory neural networks (DTBAMNNs) with multiple time-varying delays, the issue of state estimation is studied. By propose a mathematical induction method, we first investigate novel delay-dependent and -independent global exponential stability (GES) criteria of the error system. The obtained GES criteria are described by linear scalar inequalities. Then, a state observer is derived via the theory of generalized matrix inverses. These exponential stability conditions are very simple, which is convenient to verify based on the standard software tools (for example, YALMIP). Finally, we present two illustrative examples to present the effectiveness of the theoretical results. INDEX TERMSState observer; Discrete-time BAM neural network; Multiple time-varying delays; Global exponential stability; Linear scalar inequalities.

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Section: Discussionmentioning

confidence: 99%

State Estimation for a Class of Discrete-Time BAM Neural Networks With Multiple Time-Varying Delays

Cong

Zhang

2023

IEEE Access

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“…In general, time delays are composed of bounded delays and unbounded delays. Many results have been reported on the dynamic behavior analysis of INNs with bounded delays, and two main approaches, i.e., variable transformation and the non-reduced order method, were developed in [8][9][10][11]. Meanwhile, proportional delay (PD) can be considered as a special kind of unbounded delays, and in reality, it also plays a key role in, for example, the collection of current by the pantograph of an electric locomotive and the web quality of routing decisions.…”

Section: Introductionmentioning

confidence: 99%

Exponential Stability of a Class of Neutral Inertial Neural Networks with Multi-Proportional Delays and Leakage Delays

et al. 2023

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This paper investigates the exponential stability of a class of neutral inertial neural networks with multi-proportional delays and leakage delays. By utilizing the Lyapunov stability theory, the approach of parametric variation, and the differential inequality technique, some criteria are acquired that can guarantee that all solutions of the addressed system converge exponentially to the equilibrium point. In particular, the neutral term, multi-proportional delays, and leakage delays are incorporated simultaneously, resulting in a more general model, and the findings are novel and refine the previous works. Finally, one example is provided to indicate that the dynamic behavior is consistent with the theoretical analysis.

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Concise Exponential Stability Conditions for BAM Quaternion Memristive Neural Networks Affected by Mixed Delays

Zhang,

Yang,

Guan

et al. 2024

Circuits Syst Signal Process

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L2-L∞ state estimation of the high-order inertial neural network with time-varying delay: Non-reduced order strategy

Cited by 12 publications

References 29 publications

State Estimation for a Class of Discrete-Time BAM Neural Networks With Multiple Time-Varying Delays

State Estimation for a Class of Discrete-Time BAM Neural Networks With Multiple Time-Varying Delays

Exponential Stability of a Class of Neutral Inertial Neural Networks with Multi-Proportional Delays and Leakage Delays

Concise Exponential Stability Conditions for BAM Quaternion Memristive Neural Networks Affected by Mixed Delays

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