Adaptive simulation of hysteresis using neuro-Madelung model

Farrokh, Mojtaba; Dizaji, Mehrdad Shafiei

doi:10.1177/1045389x15604283

Cited by 11 publications

(7 citation statements)

References 35 publications

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“…As a result, many researchers tried to design even better models. Throughout many years, researchers managed to develop [30,31,33,34] methods based on artificial neural networks, to identify systems with hysteresis. In Kosmatopoulos et al [30], a [31] continued to study on this algorithm to improve its computational efficiency and to select more appropriate ANN structure.…”

Section: Artificial Neural Network-based Model For Identificationmentioning

confidence: 99%

“…One of their inputs was a tag, informing about the trend of the second input signal. Farrokh and Dizaji [33] continued work with MLPs. In this work, authors took into account Madelung's description of hysteresis relation, to develop identification algorithm.…”

Section: Artificial Neural Network-based Model For Identificationmentioning

confidence: 99%

“…(3) by MLP does not give good results, because it is unable to describe hysteresis with memory, i.e., for particular set of inputs, two different outputs might be required. Based on work Farrokh and Dizaji [33] and Pei et al [35], a hysteretic relation as a three argument function was developed:…”

Section: Identification Modelmentioning

confidence: 99%

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Identification of the stick and slip motion between contact surfaces using artificial neural networks

et al. 2020

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The paper presents work related to nonlinear system parameters identification. The research is focused on systems with hysteretic stiffness characteristics. The identification procedure is developed with use of artificial neural networks. The presented method assumes two separate clusters of neural networks, which are supported by additional signal processing block. Such approach gives an advantage over the conventional identification methods due to its small restrictions. The validation process considers structural responses in time and frequency domains as well as the restoring force plane of the dynamic structure. First, verification of the identification method is performed on the numerical simulation of the system with hysteretic stiffness. Next, the identification of the real dynamic system with contact-related nonlinearity is carried out. The steel samples with contacting surfaces were used in the experiment. Electromagnetic shaker was used to excite the structure and enforce a relative shear motion between surfaces in contact. The system response was recorded using the Polytec laser vibrometer.

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Section: Artificial Neural Network-based Model For Identificationmentioning

confidence: 99%

Section: Artificial Neural Network-based Model For Identificationmentioning

confidence: 99%

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Identification of the stick and slip motion between contact surfaces using artificial neural networks

et al. 2020

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“…In 2016, Farrokh and Dizaji [7], proposed a new type of multi-layer feed forward neural networks by inspiring of madelung rules and it has been called neuro-Madelung model (NMM). Madelung's rules are common among most of the hysteresis phenomena [19].…”

Section: Introductionmentioning

confidence: 99%

“…Using the learning capability of the neural networks, an adaptive general model for hysteresis is introduced according to the proposed approach. However, due to the nature of the neural network used in the NMM, and its input dependence on the input-output pair at the previous turning point and the input rate, it will be difficult to explicitly construct the inverse of the NMM [7].…”

Section: Introductionmentioning

confidence: 99%

Universal Hysteresis Identification Using Extended Preisach Neural Network

Farrokh¹,

Dizaji²,

Dizaji³

et al. 2020

Preprint

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Hysteresis phenomena have been observed in different branches of physics and engineering sciences. Therefore several models have been proposed for hysteresis simulation in different fields; however, almost neither of them can be utilized universally. In this paper by inspiring of Preisach Neural Network which was inspired from Preisach model that basically stemmed from Madelungs rules and using the learning capability of the neural networks, an adaptive universal model for hysteresis is introduced and called Extended Preisach Neural Network Model (XPNN). It is comprised of input, output and, two hidden layers. The input and output layers contain linear neurons while the first hidden layer incorporates neurons called Deteriorating Stop (DS) neurons, which their activation function follows DS operator. DS operator can generate noncongruent hysteresis loops. The second hidden layer includes Sigmoidal neurons. Adding the second hidden layer, helps neural network learn non-Masing and asymmetric hysteresis loops very smoothly. At the input layer, Besides, x(t) which is input data, ẋ(t), the rate at which x(t) changes, is included as well in order to give XPNN the capability of learning rate-dependent hysteresis loops. Hence, the proposed approach has capability of the simulation of the both rate independent and rate dependent hysteresis with either congruent or noncongruent loops as well as symmetric and asymmetric loops. A new hybridized algorithm has been adopted for training of the XPNN which is based on combination of GA and the optimization method of sub-gradient with space dilatation. The generality of the proposed model has been evaluated by applying it on various hystereses from different areas of engineering with different characteristics. The results show that the model is successful in the identification of the considered hystereses. The proposed neural network shows excellent agreement with experimental data.

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Neuro-Skins: Dynamics, Plasticity and Effect of Neuron Type and Cell Size on Their Response

Joghataie

Dizaji

2018

Neural Process Lett

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Adaptive simulation of hysteresis using neuro-Madelung model

Cited by 11 publications

References 35 publications

Identification of the stick and slip motion between contact surfaces using artificial neural networks

Identification of the stick and slip motion between contact surfaces using artificial neural networks

Universal Hysteresis Identification Using Extended Preisach Neural Network

Neuro-Skins: Dynamics, Plasticity and Effect of Neuron Type and Cell Size on Their Response

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