Synchronization of master-slave memristive neural networks via fuzzy output-based adaptive strategy

Alsaedi, Ahmed; Cao, Jinde; Ahmad, Bashir; Alshehri, Ahmed; Tan, Xuegang

doi:10.1016/j.chaos.2022.112095

Cited by 21 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Remark 4. The synchronization of integer-order MNN has been extensively studied, and various valuable results have been obtained in the literature [9][10][11][12][13][14][15][16]. However, due to the unique nonlocality and finite memory of fractional-order memristive systems, effective impulsive control methods and comparison principles for integer-order systems cannot be directly applied to fractional-order ones.…”

Section: Lemma 6 (Schur Complement)mentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

et al. 2023

View full text Add to dashboard Cite

This article addresses the issue of drive–response synchronization in fractional-order multi-link memristive neural networks (FMMNN) with multiple delays, under hybrid impulsive feedback control. To address the impact of multiple delays on system synchronization, an extended fractional-order delayed comparison principle incorporating impulses is established. By leveraging Laplace transform, Mittag–Leffler functions, the generalized comparison principle, and hybrid impulsive feedback control schemes, several new sufficient conditions are derived to ensure synchronization in the addressed FMMNN. Unlike existing studies on fractional-order single-link memristor-based systems, our response network is a multi-link model that considers impulsive effects. Notably, the impulsive gains αi are not limited to a small interval, thus expanding the application range of our approach (αi∈(−2,0)∪(−∞,−2)∪(0,+∞)). This feature allows one to choose impulsive gains and corresponding impulsive intervals that are appropriate for the system environment and control requirements. The theoretical results obtained in this study contribute to expanding the relevant theoretical achievements of fractional-order neural networks incorporating memristive characteristics.

show abstract

Section: Lemma 6 (Schur Complement)mentioning

confidence: 99%

“…The finite-time synchronization problems of uncertain MNN were addressed in [13], and more practical synchronization criteria were derived via aperiodically intermittent adjustment. Furthermore, various other research results on memristive neural networks can be found in [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In [19], the authors solved the fixed-time driver-response synchronization challenge of MNNs including complexvalued parameters. In [20], Alsaedi et al deliberated the complete synchronization of fuzzy MNNs with external perturbation by using fuzzy rules and adaptive rules. In [21], Fu et al dealt with the weak projective synchronization task for Takagi-Sugeno fuzzy MNNs with parameter mismatch based on Liapunov-Krasovsky functions and variable parameter formulas.…”

Section: Introductionmentioning

confidence: 99%

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

Fan,

Rao,

Shi

et al. 2024

Fractal Fract

View full text Add to dashboard Cite

This paper shows solicitude for the generalized projective synchronization of Caputo fractional-order uncertain memristive neural networks (FOUMNNs) with multiple delays. By extending the constant scale factor to the time-varying function matrix, we establish an extraordinary synchronization mode called time-varying function matrix projection synchronization (TFMPS), which is a generalized version of traditional matrix projection synchronization, modified projection synchronization, complete synchronization, and anti-synchronization. To achieve the goal of TFMPS, we design a novel mixed controller including the open loop feedback control and impulsive control, which employs the state information in the time-delayed interval and the sampling information at the impulse instants. It has a prominent advantage that impulse intervals are not restricted by time delays. To establish the connection between the error system and the auxiliary system, a generalized fractional-order comparison theorem with time-varying coefficients and impulses is established. Applying the stability theory, the comparison theorem, and the Laplace transform, new synchronization criteria of FOUMNNs are acquired under the mixed impulsive control schemes, and the derived synchronization theorem and corollary can effectively expand the correlative synchronization achievements of fractional-order systems.

show abstract

“…Actually, the memristor, the fourth basic element of electrical circuits, was firstly raised by Prof. Leon Chua [ 4 ] in 1971; the memristor exhibited better chaotic characteristics than the resistor in mimicking the synaptic plasticity. Depending on such excellent attributes in biologicals, many scholars have combined the memristor with neural networks (NNs) to propose the memristive neural networks (MNNs) [ 5 , 6 , 7 , 8 , 9 ] for a better understanding of the structure and functions of brain networks. Nevertheless, up to now, few researchers have conducted the memristor to the chaotic system for signal encryption and decryption, which inspired us to consider the MNNs for secure communication from the biological point of view.…”

Section: Introductionmentioning

confidence: 99%

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application

Han,

Chen,

Wei

et al. 2023

Entropy

View full text Add to dashboard Cite

In this article, the authors analyzed the nonlinear effects of projective synchronization between coupled memristive neural networks (MNNs) and their applications. Since the complete signal transmission is difficult under parameter mismatch and different projective factors, the delays, which are time-varying, and uncertainties have been taken to realize the projective synchronization of MNNs with multi-links under the nonlinear control method. Through the extended comparison principle and a new approach to dealing with the mismatched parameters, sufficient criteria have been determined under different types of projective factors and the framework of the Lyapunov–Krasovskii functional (LKF) for projective convergence of the coupled MNNs. Instead of the classical treatment for secure communication, the concept of error of synchronization between the drive and response systems has been applied to solve the signal encryption/decryption problem. Finally, the simulations in numerical form have been demonstrated graphically to confirm the adaptability of the theoretical results.

show abstract

Synchronization of master-slave memristive neural networks via fuzzy output-based adaptive strategy

Cited by 21 publications

References 27 publications

Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

Hybrid Impulsive Feedback Control for Drive–Response Synchronization of Fractional-Order Multi-Link Memristive Neural Networks with Multi-Delays

Time-Varying Function Matrix Projection Synchronization of Caputo Fractional-Order Uncertain Memristive Neural Networks with Multiple Delays via Mixed Open Loop Feedback Control and Impulsive Control

Projective Synchronization of Delayed Uncertain Coupled Memristive Neural Networks and Their Application

Contact Info

Product

Resources

About