Elman neural network-based identification of Krasnosel'skii-Pokrovskii model for magnetic shape memory alloys actuator

Xu, Rui

doi:10.1109/intmag.2017.8007946

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…To compare the modeling performance with the method by Xu and Zhou (2017), another experiment is carried out with the sinusoidal attenuation signal. The parameters are selected as k a = k b = 100, x 0 = 0:1, a = 100, and k i = 0:1.…”

Section: Resultsmentioning

confidence: 99%

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Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Zhao

Shen

et al. 2020

Journal of Intelligent Material Systems and Structures

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Rate-dependent hysteresis nonlinearity in piezoelectric actuators severely limits micro- and nanoscale system performance. It is necessary to establish a dynamic model to describe the full behavior of rate-dependent hysteresis. In this article, the Elman neural network–based hysteresis model is developed for piezoelectric actuators. An improved dynamic hysteretic operator is proposed to transform the multi-valued mapping of hysteresis into one-to-one mapping on a newly constructed expanded input space. Then, Elman neural network incorporated with the improved dynamic hysteretic operator is utilized to approximate the behavior of rate-dependent hysteresis. The combination of Elman neural network and the improved dynamic hysteretic operator can dually embody the dynamic property and is capable of fully extracting the characteristics of rate-dependent hysteresis. The experimental results are presented to illustrate the potential of the proposed modeling technique.

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Section: Resultsmentioning

confidence: 99%

“…Kilicarslan et al (2011) developed an adaptive neurofuzzy inference system (ANFIS) for hysteresis modeling. Xu and Zhou (2017) proposed the identification method of KP hysteresis model based on Elman neural networks. Yu et al (2020) proposed a rate-dependent hysteresis model based on a diagonal recurrent neural network.…”

Section: Introductionmentioning

confidence: 99%

Elman neural network–based identification of rate-dependent hysteresis in piezoelectric actuators

Zhao

Shen

et al. 2020

Journal of Intelligent Material Systems and Structures

View full text Add to dashboard Cite

show abstract

“…Automatic characterization of magnetic properties and the design of innovative materials based on the used of machine learning are catching the interest of academic researchers and development engineers in the manufacturing industry. In [113], Elman neural network was used for the identification of non-linear hysteresis model parameters. More recently, in [114] a recurrent neural network model was used to accurately predict the behavior of the hysteresis loops in ferromagnetic materials under a limited amount of measurement data available.…”

Section: Applications To the Manufacturing And Agri-food Sectorsmentioning

confidence: 99%