combination_GRFN

<code>combination_GRFN</code> combines two Gaussian random fuzzy numbers using the generalized
product-intersection rule with soft or hard normalization.

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

combination_GRFN function

<dl><dt>GRFN1</dt>
<dd>A Gaussian random fuzzy number, encoded as a list with components mu, sig
and h.</dd>
<dt>GRFN2</dt>
<dd>A Gaussian random fuzzy number, encoded as a list with components mu, sig
and h.</dd>
<dt>soft</dt>
<dd>If TRUE (default), the combination rule with soft normalization is used.
Otherwise, hard normalization is employed.</dd></dl>

Arguments

Combination of Gaussian random fuzzy numbers — combination_GRFN

<dl>

<dt>GRFN1</dt>
<dd>A Gaussian random fuzzy number, encoded as a list with components mu, sig
and h.</dd>


<dt>GRFN2</dt>
<dd>A Gaussian random fuzzy number, encoded as a list with components mu, sig
and h.</dd>


<dt>soft</dt>
<dd>If TRUE (default), the combination rule with soft normalization is used.
Otherwise, hard normalization is employed.</dd>

</dl>

combination_GRFN: Combination of Gaussian random fuzzy numbers

Description

Usage

Value

Arguments

References

See Also

Examples