GeneratorNU {FuzzyResampling} | R Documentation |
Generate initial sample using the normal and uniform distributions.
Description
GeneratorNU
generates the random sample of trapezoidal fuzzy numbers using the normal and uniform distributions (for
the "true" origin of each fuzzy number, and its increases, respectively).
Usage
GeneratorNU(n, mu, sigma, a, b, increases = FALSE, ...)
Arguments
n |
Number of fuzzy trapezoidal numbers in the created sample. |
mu |
The expected value of the normal distribution which is used to create the "true origin" of fuzzy trapezoidal number. |
sigma |
The standard deviation of the normal distribution which is used to create the "true origin" of fuzzy trapezoidal number. |
a |
The right end of the interval of the independent uniform distributions which are used to create the left and right increases of the core. |
b |
The right end of the interval of the independent uniform distributions which are used to create the left and right increases of the support. |
increases |
If |
... |
Some additional parameters to pass to other functions. |
Details
The procedure simulates the initial sample which consists of n
trapezoidal fuzzy numbers.
The "true origin" of each fuzzy number is independently drawn from the normal distribution N (mu, sigma)
.
Then increases of its core and support are independently generated from the uniform distributions
U [0,a]
and U [0,b]
(see Grzegorzewski et al. (2020)).
The output is given as a matrix. In each row, there is a single fuzzy number in one of the forms:
left end of the support, left end of the core, right end of the core, right end of the support, or
left increment of the support, left end of the core, right end of the core, right increment of the support.
To obtain this second form, the parameter increases=TRUE
has to be used.
Value
This function returns matrix which consists of n
trapezoidal fuzzy numbers.
Each fuzzy number (four values) ia given as one row in this matrix.
References
Grzegorzewski, P., Hryniewicz, O., Romaniuk, M. (2020) Flexible resampling for fuzzy data based on the canonical representation International Journal of Computational Intelligence Systems, 13 (1), pp. 1650-1662
See Also
GeneratorNExpUU
, GeneratorFuzzyNumbers
Other sampling functions:
GeneratorFuzzyNumbers()
,
GeneratorNExpUU()
Examples
# seed PRNG
set.seed(1234)
# generate 10 trapezoidal fuzzy numbers
GeneratorNU(10, 0,1,1,2)