Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties

Zhang, Shuo; Yu, Yongguang; Hu, Wei

doi:10.1155/2014/302702

Cited by 14 publications

(11 citation statements)

References 38 publications

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“…Due to the lack of theoretical results, more works for FNN were to explore the chaos and limit cycle via numerical simulations. However, as the non-existence of limit cycle in FNN was provided in [42], more researches return to theoretical innovation at the stability of FNN [43][44][45][46][47][48].…”

Section: Stability Of Fnnmentioning

confidence: 99%

A Survey of Fractional-Order Neural Networks

Zhang

Chen

2017

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications

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In this paper, the literature of fractional-order neural networks is categorized and discussed, which includes a general introduction and overview of fractional-order neural networks. Various application areas of fractional-order neural networks have been found or used, and will be surveyed and summarized such as neuroscience, computational science, control and optimization. Recent trends in dynamics of fractional-order neural networks are presented and discussed. The results, especially the stability analysis of fractional-order neural networks, are reviewed and different analysis methods are compared. Furthermore, the challenges and conclusions of fractional-order neural networks are given.

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Section: Stability Of Fnnmentioning

confidence: 99%

A Survey of Fractional-Order Neural Networks

Zhang

Chen

2017

Volume 9: 13th ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications

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“…Due to wide application in almost all branches of sciences, fractional differential equations have received remarkable attention in the field of mechanics, physics, chemistry, informatics, materials and several other applications [22,23]. To the best of our knowledge, very few results are available in literature for projection neural networks in the field of fractional calculus [24,25]. This work provides a deep analysis of stability and synchronization for a class of delayed fractional-order projection neural network.…”

Section: Introductionmentioning

confidence: 99%

Stability and synchronization of delayed fractional-order projection neural network with piecewise constant argument of mixed type

Tyagi

Abbas

2017

Tbilisi Math. J.

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Projection equations arise in several optimization problems and possess significant applications in many areas of science and engineering. In this paper, we propose a fractional-order projection neural network to solve quadratic programming problems. We study stability and synchronization for a class of delayed projection neural networks of mixed type via impulsive control. Using concepts of fractional calculus, we investigate the existence of solution and study its global asymptotic stability. Moreover, we propose an effective impulsive control scheme to achieve synchronization for the system. We demonstrate the validity and transient behaviour of the proposed neural network with the help of suitable examples.

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“…The formulation and the numerical simulations of FODNN have been carried out by many researchers [14][15][16][17]. The study of dynamic behaviors of FODNN such as bifurcation [18], stability [19], stabilization [20], synchronization [21], robust stability [22], etc., are important topics which have recently been studied and the references are cited therein.…”

Section: Introductionmentioning

confidence: 99%

Identification of Wind Turbine using Fractional Order Dynamic Neural Network and Optimization Algorithm

Aslipour

Yazdizadeh

2020

IJE

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In this paper, an efficient technique is presented to identify a 2500 KW wind turbine operating in Kahak wind farm, Qazvin province, Iran. This complicated system dealing with wind behavior is identified by using a proposed fractional order dynamic neural network (FODNN) optimized with evolutionary computation. In the proposed method, some parameters of FODNN are unknown during the process of identification, so a particle swarm optimization (PSO) algorithm is employed to determine the optimal values by which a fractional order nonlinear system can be completely identified with a high degree of accuracy. These parameters are very effective to achieve high performance of FODNN identifier and they include fractional order, initial values of states and weights of FODNN, and numerical algorithm step size for solving FODNN equation. Simulation results confirm the efficiency of the proposed scheme in term of accuracy. Furthermore, comparison of the results achieved by the proposed method and those of the integer order dynamic neural network (IODNN) depicts higher accuracy of the proposed FODNN.

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Robust Stability Analysis of Fractional-Order Hopfield Neural Networks with Parameter Uncertainties

Cited by 14 publications

References 38 publications

A Survey of Fractional-Order Neural Networks

A Survey of Fractional-Order Neural Networks

Stability and synchronization of delayed fractional-order projection neural network with piecewise constant argument of mixed type

Identification of Wind Turbine using Fractional Order Dynamic Neural Network and Optimization Algorithm

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