pl_contour

<code>pl_contour</code> computes the degree of plausibility of any number x for a given Gaussian
random fuzzy number.

An implementation of the 'Evidential Neural Network for Regression' model recently introduced in Denoeux (2023) <doi:10.1109/TFUZZ.2023.3268200>. In this model, prediction uncertainty is quantified by Gaussian random fuzzy numbers as introduced in Denoeux (2023) <doi:10.1016/j.fss.2022.06.004>. The package contains functions for training the network, tuning hyperparameters by cross-validation or the hold-out method, and making predictions. It also contains utilities for making calculations with Gaussian random fuzzy numbers (such as, e.g., computing the degrees of belief and plausibility of an interval, or combining Gaussian random fuzzy numbers).

Thierry Denoeux

evreg

Evidential Regression

pl_contour function

<dl><dt>x</dt>
<dd>The input value (can be a vector).</dd>
<dt>GRFN</dt>
<dd>A Gaussian random fuzzy number, encoded as a list with components mu, sig
and h.</dd></dl>

Arguments

Contour function of a Gaussian random fuzzy number — pl_contour

<dl>

<dt>x</dt>
<dd>The input value (can be a vector).</dd>


<dt>GRFN</dt>
<dd>A Gaussian random fuzzy number, encoded as a list with components mu, sig
and h.</dd>

</dl>

pl_contour: Contour function of a Gaussian random fuzzy number

Description

Usage

Value

Arguments

Details

References

See Also

Examples