Employing nonlinear transformation of datasets to train neural networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47718684%3A_____%2F24%3A10002350" target="_blank" >RIV/47718684:_____/24:10002350 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Employing nonlinear transformation of datasets to train neural networks

Popis výsledku v původním jazyce

Invited lecture at the Technical Computing Prague 2024. It is often a good idea to linearly transform each input and output signal to have a mean of zero and a standard deviation of one. This technique, called standardisation or z-scoring, is particularly useful for training statistical classifiers and recurrent neural networks because it helps with the conditionality and numerical stability of the training process. However, some problems require the neural network to predict outputs with the same relative error over different orders of magnitude. A typical class of such problems is modelling flow, vibration and other dynamic processes. This presentation shows how the training data can be nonlinearly transformed, how the transformation affects the network performance, and the drawbacks of this method. The application of nonlinear transformation of training data is also demonstrated by modelling hydrodynamic lubrication in a journal bearing using feedforward neural networks

Název v anglickém jazyce

Employing nonlinear transformation of datasets to train neural networks

Popis výsledku anglicky

Invited lecture at the Technical Computing Prague 2024. It is often a good idea to linearly transform each input and output signal to have a mean of zero and a standard deviation of one. This technique, called standardisation or z-scoring, is particularly useful for training statistical classifiers and recurrent neural networks because it helps with the conditionality and numerical stability of the training process. However, some problems require the neural network to predict outputs with the same relative error over different orders of magnitude. A typical class of such problems is modelling flow, vibration and other dynamic processes. This presentation shows how the training data can be nonlinearly transformed, how the transformation affects the network performance, and the drawbacks of this method. The application of nonlinear transformation of training data is also demonstrated by modelling hydrodynamic lubrication in a journal bearing using feedforward neural networks

Druh

O - Ostatní výsledky

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů