2020
DOI: 10.1051/mmnp/2019053
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Efficiency of hysteretic damper in oscillating systems

Abstract: This paper is dedicated to comparative analysis of nonlinear damping in the oscillating systems. More specifically, we present the particular results for linear and nonlinear viscous dampers, fractional damper, as well as for the hysteretic damper in linear and nonlinear (Duffing-like) oscillating systems. We consider a constructive mathematical model of the damper with hysteretic properties on the basis of the Ishlinskii-Prandtl model. Numerical results for the observable characteristics, such as the forc… Show more

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Cited by 17 publications
(7 citation statements)
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“…Thus, the reaction of systems to destructive influences applied to them depends, among other things, on their current state, and the dynamics of the system over a time interval is largely determined by its prehistory. This behavior of the system is close to the phenomena of systemic hysteresis described in the literature [20][21][22]. The use of the combinatorial model given in the article, in contrast to the models described in [1][2][3][4][5][6][7] and others, develops a set-theoretic representation of systems for solving problems related to the study of the functioning of systems under destructive conditions and external influences, namely with the minimization of information losses in the system.…”
Section: Simulation Resultsmentioning
confidence: 64%
“…Thus, the reaction of systems to destructive influences applied to them depends, among other things, on their current state, and the dynamics of the system over a time interval is largely determined by its prehistory. This behavior of the system is close to the phenomena of systemic hysteresis described in the literature [20][21][22]. The use of the combinatorial model given in the article, in contrast to the models described in [1][2][3][4][5][6][7] and others, develops a set-theoretic representation of systems for solving problems related to the study of the functioning of systems under destructive conditions and external influences, namely with the minimization of information losses in the system.…”
Section: Simulation Resultsmentioning
confidence: 64%
“…This function significantly increases the flexibility of the UNC. It is obvious that this activation function helps to enhance the robustness of the neural network to various types of noise and improves the capacity of the intellectual output by adding degrees of freedom (i.e., parameters of the hysteresis model) [11][12][13][14][15][16] which determine the nonlinearity of the dynamics of the whole ANN. It is also important that the use of hysteresis functions with feedforward neural networks results in short-term memory effects.…”
Section: Neuron Activation Functionmentioning
confidence: 99%
“…Dynamic modeling of systems with hysteresis is a complex mathematical problem attracting the attention of many researchers. Design models include the non-ideal relay, Preisach operator, and Ishlinsky model [ 41 , 42 , 43 , 44 , 45 , 46 ], and phenomenological models include the Bouc–Wen, Ivan, Duhem, etc. [ 47 , 48 , 49 ].…”
Section: Introductionmentioning
confidence: 99%