An MLP Neural Network for Approximation of a Functional Dependence with Noise

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F23%3A00373655" target="_blank" >RIV/68407700:21220/23:00373655 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-981-19-9379-4_32" target="_blank" >https://doi.org/10.1007/978-981-19-9379-4_32</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-19-9379-4_32" target="_blank" >10.1007/978-981-19-9379-4_32</a>

Result language
angličtina
Original language name
An MLP Neural Network for Approximation of a Functional Dependence with Noise
Original language description
Multilayer perceptron (MLP) neural networks used for approximation of the functional dependency are capable of generalization and thus to a limited noise removal, for example from measured data. The following text shows the effect of noise on the results obtained when data is interpolated by a neural network on several functions of two and one function of three variables. The function values obtained from the trained neural network showed on average ten times lower deviations from the correct value than the data on which the network was trained, especially for higher noise levels. The obtained results confirm the suitability of using a neural network for an interpolation of unknown functional dependencies from measured data, even when the noise load cannot be removed.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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