Outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches

Cheng, Weike; Wu, Ailong; Zhang, Jine; Li, Biwen

doi:10.1186/s13662-019-2320-0

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…Nonlinear dynamics identification is performed by neuro-fractional order Hammerstein model in [8]. Neuro-fractional order problems are solved via centralized and decentralized data sampling methods in a discrete domain [9]. Neural network based fractional order differential equations are solved for chaotic systems with primarily a control system of secondary circuit but it is added to provide essential coupling between reactor power and turbine power.…”

Section: Introductionmentioning

confidence: 99%

Fractional Order Neural Transient Modeling of Primary Circuit of ACP1000 Based Nuclear Power Plant in LabVIEW

Malik

Memon

Arshad

2022

PPASA

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The primary circuit of the nuclear power plant is the most advanced and sophisticated loop of the Advanced Chinese Pressurized Water Reactor (ACP1000). The primary circuit is composed of most technologically advanced nuclear systems and controllers. In this research work, closed loop dynamics of primary circuit (CLPC) of ACP1000 based nuclear power plant is identified. The closed loop dynamics is comprised of highly nonlinear coupled sevencontrol systems. The turbine power, pressurizer temperature, cold leg temperature, hot leg temperature, coolant average temperature and feed water flow are the selected parameters of interest as inputs while neutron power, reactor coolant pressure, pressurizer level, steam generator pressure, steam generator level and steam generator flow as outputs. Therefore, a closed loop multi-input multi-out (MIMO) is configured. The control oriented closed loop dynamics of the primary circuit of ACP1000 is estimated by state-of-the-art novel fractional order neural network (FO-ANN) tool developed in LabVIEW. The parameters of FO-ANN of CLPC (FO-ANN-CLPC) are optimized using fractional order backpropagation (FO-BP) algorithm. The performance of FO-ANN-CLPC is tested and validated in transient conditions and the proposed model predicted the desired reactor power with minimizing error function. The robust performance of the proposed closed loop model is evaluated by dynamic simulation for a prescribed turbine load power increase transient from 20 % to 100 % and validated against reactor power and behaviour of various thermal hydraulics parameters are observed and analyzed.

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Section: Introductionmentioning

confidence: 99%

Fractional Order Neural Transient Modeling of Primary Circuit of ACP1000 Based Nuclear Power Plant in LabVIEW

Malik

Memon

Arshad

2022

PPASA

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“…In [12], the optimal control problem of a system is solved. A series of notable issues have been comprehensively analysed, including out-synchronization and matrix measure approaches for stability and synchronization [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Robustness of Hopfield Neural Networks Described by Differential Algebraic Systems of Index-1 under the Conditions of Deviation Argument and Stochastic Disturbance

Liu

Yang

et al. 2021

Journal of Mathematics

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Robustness refers to the ability of a system to maintain its original state under a continuous disturbance conditions. The deviation argument (DA) and stochastic disturbances (SDs) are enough to disrupt a system and keep it off course. Therefore, it is of great significance to explore the interval length of the deviation function and the intensity of noise to make a system remain exponentially stable. In this paper, the robust stability of Hopfield neural network (VPHNN) models based on differential algebraic systems (DAS) is studied for the first time. By using integral inequalities, expectation inequalities, and the basic control theory method, the upper bound of the interval of the deviation function and the noise intensity are found, and the system is guaranteed to remain exponentially stable under these disturbances. It is shown that as long as the deviation and disturbance of a system are within a certain range, there will be no unstable consequences. Finally, several simulation examples are used to verify the effectiveness of the approach and are described below.

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A survey of fractional calculus applications in artificial neural networks

Joshi¹,

Bhosale

Vyawahare

2023

Artif Intell Rev

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Outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches

Cited by 3 publications

References 27 publications

Fractional Order Neural Transient Modeling of Primary Circuit of ACP1000 Based Nuclear Power Plant in LabVIEW

Fractional Order Neural Transient Modeling of Primary Circuit of ACP1000 Based Nuclear Power Plant in LabVIEW

Robustness of Hopfield Neural Networks Described by Differential Algebraic Systems of Index-1 under the Conditions of Deviation Argument and Stochastic Disturbance

A survey of fractional calculus applications in artificial neural networks

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