Multiple asymptotical stability analysis for fractional-order neural networks with time delays

Wan, Liguang; Zhan, Xisheng; Gao, Hongliang; Yang, Qingsheng; Ye, Mengjun

doi:10.1080/00207721.2019.1646836

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…A multilayer fractional order fault classifier is developed for X-rays image processing in LabVIEW [13]. A research is explored for stability analysis of fractional order neural time delay systems [14]. A wavelet neural network optimized fractional order PID controller is designed for Integrated PWR (IPWR) nuclear power plant load shedding studies [15].…”

Section: Framework Of Fo-ann-clpcmentioning

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: Framework Of Fo-ann-clpcmentioning

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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“…Moreover, time delays are one of the important reasons producing instability or oscillation of the systems. [18] Taking such facts into account, time delays should be considered in the FMNN. In the published papers, [17,19,20] time delays have been studied as the main considering object.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Ding

et al. 2020

Chinese Phys. B

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The finite-time Mittag–Leffler synchronization is investigated for fractional-order delayed memristive neural networks (FDMNN) with parameters uncertainty and discontinuous activation functions. The relevant results are obtained under the framework of Filippov for such systems. Firstly, the novel feedback controller, which includes the discontinuous functions and time delays, is proposed to investigate such systems. Secondly, the conditions on finite-time Mittag–Leffler synchronization of FDMNN are established according to the properties of fractional-order calculus and inequality analysis technique. At the same time, the upper bound of the settling time for Mittag–Leffler synchronization is accurately estimated. In addition, by selecting the appropriate parameters of the designed controller and utilizing the comparison theorem for fractional-order systems, the global asymptotic synchronization is achieved as a corollary. Finally, a numerical example is given to indicate the correctness of the obtained conclusions.

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Multiple O(t−α) stability for fractional-order neural networks with time-varying delays

Wan

Liu

2020

Journal of the Franklin Institute

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Multiple asymptotical stability analysis for fractional-order neural networks with time delays

Cited by 10 publications

References 44 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

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

Multiple O(t−α) stability for fractional-order neural networks with time-varying delays

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