Exponential synchronization for fuzzy cellular neural networks with time-varying delays and nonlinear impulsive effects

Pu, Hao; Liu, Yanmin; Jiang, Haijun; Hu, Cheng

doi:10.1007/s11571-015-9335-3

Cited by 13 publications

(3 citation statements)

References 31 publications

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“…In recent years, the CNNs conceptualized by Chua and Yang [1] have been widely used in many scientific and technological frontier fields, for example, associative memory, machine learning, pattern recognition, image processing, and combination optimization (see [2][3][4][5]). On the other hand, uncertainty or fuzziness cannot be ignored in the implementation of neural networks.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…So, in order to analyze the dynamical behaviors to neural networks, incorporating time delays and impulsive effects into them is a nature and necessary step. Numerous results on impulsive effects have been gained for delayed neural networks, see previous studies() and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Existence and global exponential stability of anti‐periodic solutions for delayed quaternion‐valued cellular neural networks with impulsive effects

Qin

2018

Math Methods in App Sciences

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In this paper, we consider a class of delayed quaternion-valued cellular neural networks (DQVCNNs) with impulsive effects. By using a novel continuation theorem of coincidence degree theory, the existence of anti-periodic solutions for DQVCNNs is obtained with or without assuming that the activation functions are bounded. Furthermore, by constructing a suitable Lyapunov function, some sufficient conditions are derived to guarantee the global exponential stability of anti-periodic solutions for DQVCNNs. Our results are new and complementary to the known results even when DQVCNNs degenerate into real-valued or complex-valued neural networks. Finally, an example is given to illustrate the effectiveness of the obtained results. KEYWORDSanti-periodic solution, neural networks, quaternion, stabilitywhere q 0 , q 1 , q 2 , q 3 are real numbers and the elements i, j, and k obey the Hamilton's multiplication rules:In the past two decades, the theory of quaternion has been found a lot of applications in many fields such as such as attitude control, quantum mechanics, robotics, computer graphics, and so on. 26-30 Quaternion-valued neural networks (QVNNs), Math Meth Appl Sci. 2019;42:5-23.wileyonlinelibrary.com/journal/mma

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“…Thus, it is necessary to implement discrete-time controllers. The impulsive controllers (Yang 2001;Lu et al 2013Lu et al , 2010Tan et al 2015;Qi et al 2014;Pu et al 2015) and the sampled data controllers (Chen and Francis 1995;Yu et al 2013bYu et al , 2011a are two typical types of controllers with discrete time updates. In Å ström and Bernhardsson (2002) andÅ ström (2008), the authors proposed the event-triggered controllers.…”

Section: Introductionmentioning

confidence: 99%

Event-based exponential synchronization of complex networks

2016

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In this paper, we consider exponential synchronization of complex networks. The information diffusions between nodes are driven by properly defined events. By employing the M-matrix theory, algebraic graph theory and the Lyapunov method, two kinds of distributed eventtriggering laws are designed, which avoid continuous communications between nodes. Then, several criteria that ensure the event-based exponential synchronization are presented, and the exponential convergence rates are obtained as well. Furthermore, we prove that Zeno behavior of the event-triggering laws can be excluded before synchronization being achieved, that is, the lower bounds of inter-event times are strictly positive. Finally, a simulation example is provided to illustrate the effectiveness of theoretical analysis.

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Exponential synchronization for fuzzy cellular neural networks with time-varying delays and nonlinear impulsive effects

Cited by 13 publications

References 31 publications

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

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

Existence and global exponential stability of anti‐periodic solutions for delayed quaternion‐valued cellular neural networks with impulsive effects

Event-based exponential synchronization of complex networks

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