Increasing the credibility of texts based on neuro fuzzy networks with genetic operators for regulating variables



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11 239-Article Kholmonov Namozov (Индонезия) (1)

ISSN: 2776-0987
Volume 3, Issue 3, Mar., 2022
24 
been found. Condition (a) applies to mutation and crossover, and the selection 
operator is correctly described by condition (b). 
Then the expressions 
1
)}
,...,
(
))
,...,
(
(
{
'
'
1
*
1
*


n
k
n
n
x
x
V
x
x
sel
V
p

1
)}
,...,
(
))
,...,
(
(
{
''
''
1
*
'
'
1
*


n
k
n
n
x
x
V
x
x
cross
V
p
(3) 
show that the mutation and crossover operators introduce changes in 
chromosomes, the use of which contributes to the search for the optimum. 
When a bit string of chromosomes with length 
L
is represented by the vector 
 
L
1
,
0
, a chain of bits with length 
)
(
c
L

in the chromosome represents the 
optimum, and in 
c
bits the chromosome does not coincide with the optimum. 
The probability of reaching the global optimum by the mutation and crossover 
operators for one iteration of the GA operation is then defined as 
L
L
c
P
c
L
c
1
!
)
(


,
(4) 
where !
c
is the number of permutations of elements from the possible number of 
choices 
c
L

L
/
1
is the probability that the mutation operator randomly changes bits. 
When a chromosome is represented by a vector 


L
k
1
,...,
1
,
0

, each element of 
which is taken from an alphabet of 
k
elements, then the probability of confirming 
a mutation and crossing is represented as
L
k
L
c
P
c
c
L
c
1
)
1
(
1
!
)
(




.
(5) 
If 
)
(
L
c
P
is always positive, then condition (a) is satisfied.
In this approach, the provisions of evolutionary modeling of fuzzy systems are 
based on coding, selection of optimal GA parameters, choice of membership 
function, stop criterion, and execution of fuzzy genetic operators. 
Fuzzy crossover and mutation operators are used to solve optimization problems 
on fuzzy graphs and hypergraphs. 
The next approach to expanding the capabilities and development of the system 
is the synthesis of NFNs with fuzzy GAs, which allow us to successfully solve 
structural and parametric optimization problems that arise in conditions of 
uncertain or incomplete information by forming a base of fuzzy rules, choosing 
the membership function of linguistic terms for inputs and outputs of NFNs. 



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