<code>Pl</code> computes the degree of plausibility of an interval [x,y] 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 function

<dl><dt>x</dt>
<dd>The lower bound of the interval (may be a vector).</dd>
<dt>y</dt>
<dd>The upper bound of the interval (may 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

Degree of plausibility of interval for a Gaussian random fuzzy number — Pl

<dl>

<dt>x</dt>
<dd>The lower bound of the interval (may be a vector).</dd>


<dt>y</dt>
<dd>The upper bound of the interval (may 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: Degree of plausibility of interval for a Gaussian random fuzzy number

Description

Usage

Value

Arguments

References

See Also

Examples