A novel adaptive order/parameter identification method for variable order systems application in viscoelastic soft tissue modeling

Tabatabaei, S. Sepehr; Talebi, Heidar Ali; Tavakoli, Mahdi

doi:10.1016/j.chaos.2017.04.005

Cited by 12 publications

(2 citation statements)

References 33 publications

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“…Parameter identification of chaotic systems has been studied with different approaches for discrete [ 22 , 23 , 24 , 25 ] and continuous [ 26 , 27 , 28 , 29 , 30 , 31 , 32 ] systems. In practice, three main methods are utilized, such as the synchronization method, the mathematical solution, and the optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Fractional-Order Discrete Chaotic Systems

Peng

Sun

et al. 2019

Entropy

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Research on fractional-order discrete chaotic systems has grown in recent years, and chaos synchronization of such systems is a new topic. To address the deficiencies of the extant chaos synchronization methods for fractional-order discrete chaotic systems, we proposed an improved particle swarm optimization algorithm for the parameter identification. Numerical simulations are carried out for the Hénon map, the Cat map, and their fractional-order form, as well as the fractional-order standard iterated map with hidden attractors. The problem of choosing the most appropriate sample size is discussed, and the parameter identification with noise interference is also considered. The experimental results demonstrate that the proposed algorithm has the best performance among the six existing algorithms and that it is effective even with random noise interference. In addition, using two samples offers the most efficient performance for the fractional-order discrete chaotic system, while the integer-order discrete chaotic system only needs one sample.

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Section: Introductionmentioning

confidence: 99%

Parameter Identification of Fractional-Order Discrete Chaotic Systems

Peng

Sun

et al. 2019

Entropy

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“…For instance, the fractional model of viscoelastic materials may be affected by its frequency, tension amplitude, etc. () The fractional orders of the thermal process could be influenced by the outside temperature change or the heterogeneous transfer medium . A practical electrical system, which contains various types of fractional‐order components, may have uncertain orders …”

Section: Introductionmentioning

confidence: 99%

Robust stabilization of fractional‐order plant with general interval uncertainties based on a graphical method

Zheng

2017

Intl J Robust & Nonlinear

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This paper concentrates on the robust stabilization of fractional-order plant suffering from general interval uncertainties by using fractional-order controllers.General interval uncertainties mean that the coefficients and orders of the denominator and nominator of the fractional-order plant are all uncertain and lie in specified intervals. Two main contributions are presented, as follows.(i) Based on a graphical method, a necessary and sufficient criterion is proposed for the stabilization of the general interval fractional-order plant. By adopting some well-defined multivalue functions, the proposed method can explicitly construct the nonconvex boundary of the value set of the fractional system.(ii) Two alternative methods are presented to improve the computational efficiency of the stabilization test. Based on a newly developed redundancy elimination technique, the first method can avoid computing and plotting many segments, which are in the interior of the value set of the fractional system. The second method utilizes a novel convex polygon bounding approach. It can efficiently construct a convex polygon to bound the nonconvex value set of the general interval fractional system. Examples are followed to illustrate the effectiveness of the proposed methods.

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Optimal quasi-synchronization of fractional-order memristive neural networks with PSOA

Zhang

Yang

2019

Neural Comput & Applic

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A novel adaptive order/parameter identification method for variable order systems application in viscoelastic soft tissue modeling

Cited by 12 publications

References 33 publications

Parameter Identification of Fractional-Order Discrete Chaotic Systems

Parameter Identification of Fractional-Order Discrete Chaotic Systems

Robust stabilization of fractional‐order plant with general interval uncertainties based on a graphical method

Optimal quasi-synchronization of fractional-order memristive neural networks with PSOA

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