Stabilization for a class of delayed switched inertial neural networks via non-reduced order method

Chen, Xuan; Lin, Dongyun

doi:10.1177/0142331220985944

Cited by 6 publications

(3 citation statements)

References 34 publications

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“…However, these simple delays are no longer suitable for increasingly complex neural network system (NNS), so mixed delays have attracted more and more attention in the modeling of NNS. Although many achievements have been made in the dynamic behavior analysis of delayed NNs (see Chen and Lin, 2021; Hai, 2022; Kashkynbayev et al, 2019; Wu et al, 2021; Xu and Li, 2018; Zeng et al, 2016), NNs with mixed delays are rarely studied.…”

Section: Introductionmentioning

confidence: 99%

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Yang

Zou

Liu

2023

Transactions of the Institute of Measurement and Control

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In this paper, the finite-time (FT) and fixed-time synchronization (F-TS) problems of fuzzy inertial cellular neural networks (FICNNs) with mixed delays are discussed based on the Filippov solution theory and FT stability theory. Compared with related research, this paper considers the fuzzy inertial system combined with discontinuous activation of cellular neural networks (CNNs) for the first time. Then, by choosing appropriate variable transformation, the original system can be rewritten as a first-order differential system. Some novel and useful sufficient conditions for finite/fixed-time synchronization of FICNNs are established by applying three suitable discontinuous state feedback controllers. Finally, three numerical simulations are presented to elaborate the validity of the proposed methods.

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Section: Introductionmentioning

confidence: 99%

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Yang

Zou

Liu

2023

Transactions of the Institute of Measurement and Control

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“…The stability results of neural networks with controller are less conservative and more convenient to be applied. This kind of neural networks have also obtained a large number of theoretical research achievements (see earlier studies [27][28][29][30][31][32]). It is worth noting that the neural networks with controller mainly rely on adjusting the gain matrix of controller to make its system possess steady-state output.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis based on a control adjuster for switched neural networks by trajectory similarity

Zhou

Zhang

2023

Math Methods in App Sciences

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This paper focuses on a class of exponential stability control problem for the stochastic neural networks (SNNs) with time‐varying delays and Markov jump (TVDMJ). As a prerequisite to the main theorem, the existence and uniqueness of the solution for the main system are proved via contraction map principle. For the stability control of the system, we design a controller which can be adjusted the parameters to make the system stabilization. We use this controller to actively control the stability behavior of the system, rather than the papers by setting the appropriate parameters to make the system achieve a stable state. Based on the intermittent control, we design a novel trajectory similarity process control and obtain exponential stability results, which are less conservative than the existing theoretical results. Two examples based on some numerical calculation and simulation diagrams are presented to validate the effectiveness for the utilized techniques.

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“…The fixed-time (Zhou et al, 2022), adaptive stabilization (Bekiaris-Liberis et al, 2013), and local stabilization problems Chen and Wang (2021) were, respectively, investigated in the literature. Meanwhile, for the nonlinear time-delay systems (Mazenc et al, 2012) and the switching time-delay systems (Ghaderi et al, 2022; Chen and Lin, 2021), the (global) stabilization problem was addressed in Mazenc et al (2012), Ghaderi et al (2022), and Chen and Lin (2021).…”

Section: Introductionmentioning

confidence: 99%

Delay compensation of linear systems with multiple distributed input delays via memoryless feedback

Liu

Yang

2023

Transactions of the Institute of Measurement and Control

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Time-delay systems are widely used in industrial applications, such as chemical process and aerospace. The predictor-based feedback method is one of the effective ways to deal with such systems. This paper studies the delay compensation problem for linear systems with multiple distributed input delays. A novel state vector associated with the future state of the system is constructed, which reduces the original linear time-delay system to a linear delay-free system. Memory feedback controller is then designed, and necessary and sufficient conditions for the stability of the closed-loop system are acquired in accordance with the stability of an integral delay system. To avoid the difficult implementation problem of memory feedback controller, the memoryless feedback controller is proposed instead to compensate the multiple distributed input delays. Finally, the proposed controllers are applied to the design of the linearized liquid-level control resonant circuit system.

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Stabilization for a class of delayed switched inertial neural networks via non-reduced order method

Cited by 6 publications

References 34 publications

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Finite/fixed-time synchronization control of fuzzy inertial cellular neural networks with mixed delays

Stability analysis based on a control adjuster for switched neural networks by trajectory similarity

Delay compensation of linear systems with multiple distributed input delays via memoryless feedback

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