Fixed-Time Synchronization of Fuzzy Cellular Neural Networks With Time-Varying Delays and Discontinuous Activations

Liu, Yihong; Sun, Yumei

doi:10.1109/access.2020.2985212

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…In practical applications, uncertainty or ambiguity is a frequently occurring phenomenon. The fuzzy neural networks mentioned in References 25,34 is more practical, so we will consider the fixed‐time synchronization of fuzzy neural networks with stochastic perturbations in the future; 2. Due to the increasing importance of aperiodically intermittent control, 35,36 in future work, the fixed‐time synchronization of CNs via aperiodically intermittent control is also one of the topics we are interested in.…”

Section: Discussionmentioning

confidence: 99%

Fixed‐time synchronization of stochastic complex networks with Markov jump and mixed delays via adaptive non‐chattering control

Liu

Xiao-li

2021

Optim Control Appl Methods

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In this article, the fixed‐time synchronization problem for stochastic complex networks (CNs) with Markov jump and mixed delays is investigated. Compared with fixed‐time synchronization of stochastic CNs in existing literature, the fixed‐time synchronization for stochastic CNs with Markov jump and mixed delays is studied for the first time, which is of better significance. Utilizing the Lyapunov method, under the framework of adaptive non‐chattering control, some fixed‐time synchronization conditions are derived. Besides, from the results of the convergence time, the convergence time obtained by the fixed‐time synchronization studied in this article does not depend on the initial value of the system, but is only related to the control parameters. Eventually, a numerical example is also carried out to demonstrate the practicability and validity of our proposed results. Furthermore, a secure communication synchronization problem is presented to illustrate the effectiveness of the obtained results.

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Section: Discussionmentioning

confidence: 99%

Fixed‐time synchronization of stochastic complex networks with Markov jump and mixed delays via adaptive non‐chattering control

Liu

Xiao-li

2021

Optim Control Appl Methods

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“…Some related mathematical concepts are covered in this section, including the definition of fractional calculus, the solution of the fractional differential equation in Filippov's sense [36,37], and the theory of fractional differential equations.…”

Section: Problem Formulation and Preliminariesmentioning

confidence: 99%

Fixed-Time Synchronization for Fractional-Order Cellular Inertial Fuzzy Neural Networks with Mixed Time-Varying Delays

Sun,

Liu,

Liu

2024

Fractal Fract

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Due to the widespread application of neural networks (NNs), and considering the respective advantages of fractional calculus (FC), inertial neural networks (INNs), cellular neural networks (CNNs), and fuzzy neural networks (FNNs), this paper investigates the fixed-time synchronization (FDTS) issues for a particular category of fractional-order cellular-inertial fuzzy neural networks (FCIFNNs) that involve mixed time-varying delays (MTDs), including both discrete and distributed delays. Firstly, we establish an appropriate transformation variable to reformulate FCIFNNs with MTD into a differential first-order system. Then, utilizing the finite-time stability (FETS) theory and Lyapunov functionals (LFs), we establish some new effective criteria for achieving FDTS of the response system (RS) and drive system (DS). Eventually, we offer two numerical examples to display the effectiveness of our proposed synchronization strategies. Moreover, we also demonstrate the benefits of our approach through an application in image encryption.

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“…Remark 5.3. The fuzzy neural networks are investigated in [7,29,47]. These models are first order differential equations without neutral terms and distributed time-varying delays.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

Aouiti

Hui

Jallouli

et al. 2021

Fuzzy Sets and Systems

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This paper addresses the problem of fixed-time stabilization for a class of fuzzy neutral-type inertial neural networks (FNTINNs) with time-varying delay. By using a novel fixed-time stability theorem for dynamical systems, two different feedback control laws are designed to ensure the fixed-time stabilization of FNTINNs with time-varying delay. The proposed theoretical results can lead to a better upper settling-time estimation compared to existing results. Finally, three simulation examples are provided to illustrate the validity of the proposed theoretical results.

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Fixed-Time Synchronization of Fuzzy Cellular Neural Networks With Time-Varying Delays and Discontinuous Activations

Cited by 10 publications

References 35 publications

Fixed‐time synchronization of stochastic complex networks with Markov jump and mixed delays via adaptive non‐chattering control

Fixed‐time synchronization of stochastic complex networks with Markov jump and mixed delays via adaptive non‐chattering control

Fixed-Time Synchronization for Fractional-Order Cellular Inertial Fuzzy Neural Networks with Mixed Time-Varying Delays

Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

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