Finite-time Mittag–Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions*

Chen, Chong; Ding, Zhixia; Li, Sai; Wang, Liheng

doi:10.1088/1674-1056/ab7803

Cited by 17 publications

(8 citation statements)

References 52 publications

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“…[1][2][3][4][5] Most of the related results concentrate on real-valued neural networks (RVNNs). [6][7][8] Notably, the research direction of neural networks is changing from the traditional real-valued domain to the complex-valued domain, and some scholars are beginning to study complex-valued neural networks (CVNNs). [9] Compared with RVNNs, CVNNs have more complex characteristics and wider practical applications, which make it possible to solve those problems that RVNNs cannot, for instance, the problem of XOR [10] and the detection problem of symmetry.…”

Section: Introductionmentioning

confidence: 99%

Finite-time Mittag–Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Wang¹,

Ding²,

Li³

et al. 2022

Chinese Phys. B

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Without dividing the complex-valued systems into two real-valued ones, a class of fractional-order complex-valued memristive neural networks (FCVMNNs) with time delay is investigated. Firstly, based on the complex-valued sign function, a novel complex-valued feedback controller is devised to research such systems. Under the framework of Filippov solution, differential inclusion theory and Lyapunov stability theorem, the finite-time Mittag-Leffler synchronization (FTMLS) of FCVMNNs with time delay can be realized. Meanwhile, the upper bound of the synchronization settling time (SST) is less conservative than previous results. In addition, by adjusting controller parameters, the global asymptotic synchronization of FCVMNNs with time delay can be also realized, which improves and enrich some existing results. At last, some simulation examples are designed to verify the validity of conclusions.

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Section: Introductionmentioning

confidence: 99%

Finite-time Mittag–Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Wang¹,

Ding²,

Li³

et al. 2022

Chinese Phys. B

Self Cite

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“…For instance, the author [29] investigated the FTMLS of memristive BAM FONNs with time delays via state-feedback control. Chen et al [30] studied FTMLS of memristor-based FONNs with parameters uncertainty by using Lyapunov-like method.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Feedback Controller Design for Finite-Time Mittag-Leffler Synchronization of Fractional-Order Quaternion-Valued Reaction-Diffusion Fuzzy Molecular Modeling of Delayed Neural Networks

et al. 2021

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This paper addresses an adaptive fuzzy feedback controller design problem for finite-time Mittag-Leffler synchronization (FTMLS) of fractional-order quaternion-valued reaction-diffusion T-S fuzzy molecular modeling of delayed neural networks. A novel approach is proposed to effectively deal with the joint effects from fuzzy rules and reaction-diffusion terms for the class of T-S fuzzy fractional-order reaction-diffusion delayed quaternion-valued neural networks (FORDDQVNNs) under consideration. By employing Lyapunov stability theory, Caputo fractional derivative, several algebraic criteria are established to guarantee the FTMLS of T-S fuzzy FORDDQVNNs via designed fuzzy feedback controller. Moreover, the adaptive controller and parameter update laws are designed via adaptive control methods. Compared with existing results in the literature, we also show that our results are less conservative than existing ones with these illustrative T-S fuzzy FORDDQVNNs. A numerical example is presented to verify the analysis results and illustrate the effectiveness of the proposed FTMLS conditions.INDEX TERMS Quaternion-valued neural networks (QVNNs), Fractional derivatives, Reaction-diffusion terms, Takagi-Sugeno fuzzy, Adaptive control law.

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“…Compared with traditional neural networks, some experiment results have demonstrated that SDSNNs can imitate the synaptic activity better in the brain emulation. Accordingly, an enormous interest has been attracted to the investigation of SDSNNs and many pioneering works have been presented, such as synchronization, [3][4][5][6][7][8][9][10] state estimation. [11][12][13][14] It is noteworthy that stability is a prerequisite for ensuring SDSNNs steadily operation.…”

Section: Introductionmentioning

confidence: 99%

“…From a resource conservation of view, these traditional control schemes in Refs. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] will lead to transmission burdens and increase control cost. With the increasing demand of digital technology in industry, a rich synthesis of energy-saving networked control schemes, such as impulsive control, [20][21][22] sampled-data control, [23][24][25][26] and event-triggered control, [27][28][29][30][31][32][33][34][35][36][37][38][39][40] compose a popular research issue in relevant applications domains.…”

Section: Introductionmentioning

confidence: 99%

Discontinuous event-trigger scheme for global stabilization of state-dependent switching neural networks with communication delay*

Fan¹,

Wang²,

Xia³

et al. 2021

Chinese Phys. B

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This paper is concerned with the global stabilization of state-dependent switching neural networks (SDSNNs) via discontinuous event-triggered control with network-induced communication delay. Aiming at decreasing triggering times, a discontinuous event-trigger scheme is utilized to determine whether the sampling information is required to be sent out or not. Meanwhile, under the effect of communication delay, the trigger condition and SDSNNs are transformed into two tractable models by designing a fictitious delay function. Then, using the Lyapunov–Krasovskii stability theory, some inequality estimation techniques, and extended reciprocally convex combination method, two sufficient criteria are established for ensuring the global stabilization of the resulting closed-loop SDSNNs, respectively. A unified framework is derived that has the ability to handle the simultaneous existence of the communication delay, the properties of discontinuous event-trigger scheme, as well as feedback controller design. Additionally, the developed results demonstrate a quantitative relationship among the event trigger parameter, communication delay, and triggering times. Finally, two numerical examples are presented to illustrate the usefulness of the developed stabilization scheme.

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Finite-time Mittag–Leffler synchronization of fractional-order delayed memristive neural networks with parameters uncertainty and discontinuous activation functions*

Cited by 17 publications

References 52 publications

Finite-time Mittag–Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Finite-time Mittag–Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Adaptive Fuzzy Feedback Controller Design for Finite-Time Mittag-Leffler Synchronization of Fractional-Order Quaternion-Valued Reaction-Diffusion Fuzzy Molecular Modeling of Delayed Neural Networks

Discontinuous event-trigger scheme for global stabilization of state-dependent switching neural networks with communication delay*

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