2020
DOI: 10.1134/s2070048220020040
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Hysteretic Converters with Stochastic Parameters

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Cited by 11 publications
(6 citation statements)
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“…Therefore, we come to the following conclusion: in the production model (16) for any finite time moment T in the interval [0, T], there is a single solution c * (t) to the system (34), which is strictly monotonous on [0, T].…”
Section: U(t) P(t)mentioning
confidence: 91%
See 1 more Smart Citation
“…Therefore, we come to the following conclusion: in the production model (16) for any finite time moment T in the interval [0, T], there is a single solution c * (t) to the system (34), which is strictly monotonous on [0, T].…”
Section: U(t) P(t)mentioning
confidence: 91%
“…Thus, [32] investigated the response of a non-linear system to stochastic external factors, while [33] modified the said model to the threshold numbers characterised by random, rather than deterministic variables. In [34], hysteretic operators with stochastic parameters are discussed.…”
Section: Purpose/backgroundmentioning
confidence: 99%
“…However, the internal parameters of the hysteresis subsystem in these works were considered deterministic. A generalization of one of the simplest hysteresis model such as a generalized backlash has been considered in [95][96][97]: its defining curves were assumed to be subject to random disturbances, and the output of such a converter was treated as a random process. In the above papers, the correctness of the corresponding definition was proved and the basic properties of a stochastic converter were studied.…”
Section: Stochastic External Excitation and Non-deterministic Systemsmentioning
confidence: 99%
“…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%