A fractional-order quantum neural network: dynamics, finite-time synchronization

Wang, S-F; Xu, X-J

doi:10.1088/1402-4896/acfc31

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…The Fractional-order quantum cellular neural networks (FOQCNNs) are a new type of QCNNs that has been proposed recently. Wang and Xu first studied the time-fractional-order quantum cellular neural network with uncertainties and external disturbances and analyze its dynamic behavior under finite-time synchronization using terminal sliding mode control [18]. However they have not discussed the definition of the mentioned fractional derivative, nor have they shown the derivation of the given dynamic equations.…”

Section: Introductionmentioning

confidence: 99%

Power-exponential and fixed-time consensus of conformable fractional-order quantum cellular neural networks via event-triggered control

Xiong,

Li,

Xiong

et al. 2024

Phys. Scr.

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Quantum neural networks (QNNs) are considered to be superior to classical ANNs in machine learning, memory capacity, information processing, and quantum system simulation. However, In a practical and complex system, the dynamic behavior of an open quantum system could not be accurately described by an integer-ordered Schrödinger equation. In this paper, the conformable time-fractional-order Schrödinger equation is proposed, and accordingly, the model of conformable fractional-order quantum cellular neural networks (CFOQCNNs) is established and derived from the as-proposed equation. The properties of the conformable fractional-order derivative are studied and several new inequalities regarding the power-exponential and fixed-time convergence of conformable fractional-order systems are obtained. To save the communication resource, we introduce the event-triggered mechanism to construct the controllers and then the power-exponential and fixed-time synchronizations of the master-slave systems derived from the above CFOQCNNs are studied. We also prove the absence of Zeno behaviors regarding the event-triggered strategies. According to the numerical simulation, the dynamic behavior of the CFOQCNNs is discussed and the dissipativity of the CFOQCNNs is briefly revealed. Then the synchronization behaviors of the master and slave CFOQCNNs under power-exponential and fixed-time event-triggered control are demonstrated, where the effectiveness of the event-triggered control strategy is verified. Control behaviors with different fractional orders are also presented. We also discuss the hybrid of power-exponential control and fixed-time control and illustrate the advantages of the hybrid strategy. In the last, we conclude our studies, analyze the drawbacks of this work, and briefly introduce our future research.

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Section: Introductionmentioning

confidence: 99%

Power-exponential and fixed-time consensus of conformable fractional-order quantum cellular neural networks via event-triggered control

Xiong,

Li,

Xiong

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

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Adaptive control for memristive system via compensatory controller and Chebyshev neural network

Wang

2024

Sci Rep

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In this paper, based on linear matrix inequality technique, a simple controller and a compensatory controller are designed. It can track arbitrary fixed points and any periodic orbits. In addition, a synchronization control method via Chebyshev neural network with external disturbances is proposed. An adaptive controller is given. The Chebyshev neural network is used to approximate the uncertain nonlinear function and the adaptive law is used to adjust the corresponding parameters in the system. Taking a 4D memristive chaotic system as examples, the results are in consistent with the simulations. From a framework and control theoretical point of view, the proposed synchronization approach via compensation controller and Chebyshev neural network is firstly presented. From an application point of view, the proposed scheme can simplify the complexity of controller design. It is promising in many applications for mem-systems as secure communications and neural networks.

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A fractional-order quantum neural network: dynamics, finite-time synchronization

Cited by 2 publications

References 29 publications

Power-exponential and fixed-time consensus of conformable fractional-order quantum cellular neural networks via event-triggered control

Power-exponential and fixed-time consensus of conformable fractional-order quantum cellular neural networks via event-triggered control

Adaptive control for memristive system via compensatory controller and Chebyshev neural network

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