Retraction Note: Parameter estimation of network signal normal distribution applied to carbonization depth in wireless networks

For the average state of the normal distribution parameter estimation, regular normal distribution parameter gives an estimation, but the carbonation depth of influence factors is more of a parameter estimation, shooting low deficiencies; therefore, putting forward application in the carbonation depth of the normal distribution parameter is estimated. A normal distribution parameter estimation model is constructed, and a normal distribution parameter estimation model framework is constructed by using the least squares method to determine the expression of normal distribution parameters. Based on the linear deviation calculation of normal distribution parameters and the determination of the maximum similar value of parameters, the parameter estimation is realized by using the Bayesian function of carbonization depth. The parameter estimation of network signal based on carbonization depth is proposed. Parameter estimation can play an important role in the intelligent analysis of big data, and it is also an important basic guarantee for machine learning algorithms. Using the integrity test results and error rate test result, variable parameters calculated from measured parameters, substitution shooting parameters calculation formula of parameter estimation is put forward by the conventional parameter estimation methods, which shot up to 22.12%, is suitable for the carbonation depth of the normal distribution parameter estimation.


es of the nor
al distribution.In order to ensure the effectiveness of the design of the normal distribution parameter estimation method, the type of carbide simulation test environment uses two different kinds of normal distribution parameter estimation method, which is applied to the carbonation depth of the normal distribution parameter estimation simulation test.The test results show that the normal distribution parameter estimation method is proposed with high effectiveness.

The rest of this paper is organized as follows: Section 2 discusses the construction of the normal distribution parameter estimation model, followed by the normal distribution parameter estimation of carbonization depth in Section 3. The example analysis is discussed in Section 4. Section 5 concludes the paper with summary and future research directions.


The proposed algorithm


The framework of normal distribution parameter estimation model is established

Setting up the normal distribution parameter estimation model framework was originally done in 1733, by a German mathematician and astronomer, Abraham, dermot foer (Abraham DE Moivre), which was proposed for the first time.Laplace (Marquis DE Laplace) and Gaussian (Carl Friedrich Gauss), on the normal distribution, have also made a contribution to the research.First of all, the Gaussian distribution is applied to astronomical research [3], which is used to study the error theory.Laplace associated with the central limit theorem; "yuan" error theory was put forward for the first time.After their pioneering work, there has been more and more scientific workers to the normal distribution, which is widely used in the parameter estimation of carbonation depth.Through the efforts of scholars' research, the least squares met od was finally developed.It is applied to the theory of probability and mathematical statistics; besides, the normal distribution is widely used in practice.

Assuming that the random variable x is normally distributed, the probability density function is
f ðxÞ ¼ 1 ffiffiffiffi 2π p expf− ðx−μÞ 2 2σ 2 g; −∞ < x < ∞;(1
) in which, μ is called the mean valu

e variance parameter, and -∞ < μ < ∞, σ > 0 is satisfied, which is written as x
N(μ, σ 2 ).The probability density function graph of its normal distribution is shown in Fig. 1.The normal distribution function is [4]
FðxÞ ¼ 1 ffiffiffiffi 2π p R x −∞ e ðx−μÞ 2 2σ 2 dt (2)
As can be seen from the graph of probability density function of normal distribution in Fig. 1, the f(x) curve is a bell curve that is symmetric about x = μ.The characteristic is two, whose ends are low, middle is high, and both sides are symmetrical.When x equals to x, f(x) can get maximum 1= ffiffiffiffiffiffiffiffi 2πσ p

. When x closes to ± ∞, f(x) closes to 0. The Finally, the normal distribution parameter estimation model framework can be expressed a s [5] follows:

If random variable x~N(μ, σ 2 ), it comes x−μ σ $ Nð0; 1Þ, making x 1 , x 2 , …, x n a sample table from a normal distribution of x~N(μ, σ 2 ) .The normal distribution parameter estimation model framework can be expressed as
s 2 ¼ θ n−1 X n i¼1 x i −X À Á 2ð3Þs 2 ¼ θ n−1 X n i¼1 x i −X À Á 2ð4Þ
Including (1) Mutual indepen ence from X and s 2 ,

(2) X $ Nðμ; σ 2 =nÞ, and

(3) ðn−1Þs 2 σ 2 $ χ 2 ðn−1Þ


Determine the ex ruction of the normal distribution parameter estimation model framework, analysis framework of asymptotic distribution depends on the unknown parameter theta.When the sample size is small, the critical value which is determined by the limit distribution of inspection efficiency is lower.

tor consisting of one or more unknown parameters, Θ is parameter
pace.If x 1 ,x 2 ,...,x n are the samples from the totality, L(θ; x 1 , x 2 , …, x n ) is taken as the joint probability density function of the sample, which is recorded as L(θ), so equation 18 is as follows:
L θ ð Þ ¼ θ; x 1 ; x 2 ; …; x n ð Þ ¼f x 1 ; θ ð Þf x 2 ; θ ð Þ… f x n ; θ ð Þð18Þ
In this equation, L(θ) is named as sample likelihood function.If some statistic θ ¼ θð
x 1 ; x 2 ; …; x n ÞL θ ð Þ ¼ Y n i¼1 f x i ; μ; σ 2 À Á ¼ 1 ffiffiffiffiffiffiffiffi 2πσ p n exp − X n i¼1 x i −μ ð Þ 2 2σ 2 9 > > > = > > > ; 8 > > > < > > > :ð19Þ
The logarithmic likelihood function is
ln L θ ð Þ ¼ n 2 ln 2πσ 2 À Á − 1 2σ 2 X n i¼1 x i −μ ð Þ 2ð20Þ
Take the derivative of the above two parameters,
∂ ln L θ ð Þ ∂σ 2 ¼ 1 σ 2 X n i¼1 x i −μ ð Þ 2ð21Þ∂ ln L θ ð Þ ∂σ 2 ¼ −n 2σ 2 þ 1 2σ 4 X n i¼1 x i −μ ð Þ 2 ¼ 0ð22Þ
The maximum similarity of normal distribution parameters,
μ i −X 2 ¼ n−1 n s 2ð24Þ
3.3 The Bayesian function of carbonization depth was established to estimate the parameters According to the normal distribution of the Bayesian statistics from the a priori knowledge about the general information, carbonation dep n of normal distribution, the sample information, and three kinds of information to carry on the statistical inference, which rely on the normal distribution parameter of the linear deviation, the nor al distribution parameter of the enormous similarity values, and the discriminant.Bayesian for any unknown variable is the most fundamental po ed as a random variable.Besi describe the unknown situation of θ is cal on form and process of the Bayesian formu r the population that depends on the density function of the parameter.As θ c space Θ is random var presents conditions of density of X when θ is determined.f(x; θ) is written as f(x|

ction when the random variable θ gives a specific value to totality X.

T
en, the prior distribution π(θ) is selected according to the prior information of parameters θ.

Next, from Bayes' point of view, the sample x = (x 1 , x 2 , …, x n ) is produced by two steps.

First, a sample θ' is generated from the prior distribution π(θ) determined in step 2. Then x = (x 1 , x 2 , …, x n ) is generated from f(x; θ').At this point, the joint conditional probabili jθ 0 ð Þ ¼ Y n i¼1 f x i jθ 0 ð Þð25Þ
In Eq. ( 25), the sample information and the overall information are integrated, so it is called the likelihood function.Because θ' of the third step is an unknown hypothesis, which is based on the selected prior distribution, all possibilities of θ' should be considered and the joint distribution of samples x and parameters θ should be obtained.
n parameters needs to be calculated.Whe rs according to the prior distribution.After gett is decomposed:
h x; θ ð Þ ¼ π θjx ð Þm x ð Þð27Þ
m(x) is m 28Þ
There is not any information about θ in the equation.π(θ| x) makes inference to θ.In this point, the equation of π(θ| x) is as follows:
π θjx ð Þ ¼ h x; θ ð Þ m x ð Þ ¼ f xjθ ð Þπ θ ð Þ R Θ f xjθ ð Þπ θ ð Þdθ ¼ cf xjθ ð Þπ θ ð Þð29Þ
In this equation, c has nothing to do with θ.Equation 29 is the form of the probability density function of the Bayesian formula.Set in the sample x, parameter θ of the conditional distribution is called the posterior distribution.It focuses on the overall, sample, and all the related parameters of a priori information, and it has ruled out all information which has nothing to do with the parameters of the result.Therefore, based on the posterior distribution θ of parameters,π(x| θ) of statistical inference can be improved and be more effective [17].

Assuming θB to be the Bayesian of θ estimation, comprehensive nformation is about various posterior distribution.The information is extracted from π(θ| x) to get the results of θB .When the loss function is square loss, a commonly used standard of Bayesian estimation is to minimize it with the correct posterior mean square error criterion MSE.

MESð θB jxÞ ¼ E θjx ð θB −θÞ 0 ð θB −θÞ
¼ Z Θ θB −θ 2 π θjx ð Þdθ ¼ θ2 B −2 θB Z Θ θπ θjx ð Þdθ þ Z Θ θ 2 π θjx ð Þdθ E θ |
x stands for the minimum value of the expectations with posterior distribution.It can be see that the type is a quadratic trinomial of θB ; and binomial coefficient is positive.Therefore, here will be a minimum, and the minimum value is as follows.
θB ¼ Z Θ θπ θjx ð Þdθ ¼ E θjx θjx ð Þð30Þ
Through the type, it can be seen on the mean square error criterion that the Bayesian estimation of parameter theta θ is the posterior mea rameter of the Bayesian estimation problem, according to the principle of Bayesian estimation, the estimation is for the posterior distribution function of expectations.In this paper, the posterior distribution calculation is simplified used fully in statistics for the computation of the posterior distribution [18]:
μB ¼ ∬ μπ θjY ð Þdθ σ2 B ¼ ∬ σ integral, it is difficult to directly calculate the Bayesian estimation of the explicit solution of theta.At the same time, MCMC method is conducted in numerical simulation under different prior [19].

Using Devroye's thoughts obey the nuclear formula of distribution of the parameters θ and conditions of sample σ 2 , combining German algorithms to calculate and determine the carbonation depth of t cess is as follows [20]:

(1) Few p 0 ; σ 2 0 , and will be the first step j and, respectively, for μ j and σ 2 to μ j and σ 2 j ; (2) Produce obedience
μ j + 1 from π 1 ðμj ep 3 N times by calculating the Bayesian estimates l(μ, σ 2 ) through 1
N−m 0 P N j¼m 0 þ1 lðμ; σ 2 j Þ , including
those for debugging.Based on the normal distribution parameter of the linear deviation calculation, which is similar to the parameters of great value to determine, the application in the carbonation depth of the Bayesian parameter estimation function is implemented [21].


Experiment test and result analysis

In order to guarantee the normal distribution in this paper, the determination of carbonation depth deviation estimates the validity o the simulation experiments analysis [22].During the trial, there is a different type of carbide as the test object, and the normal distribution parameter estimation of carbonation depth is simulated in the test.On the depth of different types of carbide, as well as the environment and carries in the simulation guarantees the validity of the test.The use of conventional Gaussian distribution parameter estimation method for comparison object compare the simulation results.The test data is presented in the same data the accuracy of the simulation test, simulation process is used to provide different types of carbide as test objects.Using two different normal distribution parameter estimation methods is the normal distribution parameter estimation of carbonation depth simulation experiment.The experiment results are analyzed due to the different methods of analysis results, and the analysis methods are different.Therefore, test process is to guarantee the environmental parameters.In this paper, the results of the test data set are shown n the above-mentioned operation environment.Loading simulation data type carbide and imitating the loading type carbide simulation parameters are as follows (Table 2):


Test process design

In order to verify the hit ratio of normal distribution parameters of two different normal distribution parameter estimation methods, the integrity test of normal distribution estimation results was carried out.The error rate test of the results and the normal distribution estimation esults were carried out, and the two test results were recorded.The error rate of the normal distribution parameters was calculated according to the probability formula of the normal distribution parameters, and the comparison was made.

First of all, the prepared data were inputted into the computer simulation system, and the computer simulation system was set up in accordance with the requirements in Table 1 to perform correlation operations.

Then, in the same time period, under the same test environment and the same influence parameters, the integrity test of the normal distribution estimation results is carried out.Otherwise, the error rate test of the normal distribution est

ation results is conducted.

Finally
the third party analysis and recording software is used to analyze the relevant data generated by the computer simulation equipment.Meanwhile, simulation of laboratory personnel operation and simulation of computer equipment factors of uncertainty are eliminated.The simulation test of normal distribution parameters of carbonization depth was carried out for different types of carbonization and different normal distribution parameter estimation methods.The results are shown in the comparison result curve of this test and weighted analysis is carried out.The experimental results are obtained by using the normal distribution parameter hit ratio calculation formula.


Test analysis of normal distribution estimation

In the test process, two different carboniz

ion types and dif
erent normal distribution parameter estimation methods were used to carry out the integral test analysis of normal distribution estimation results.The comparison curve of the overall test results of the normal distribution estimation results is shown in Fig. 6.

According to the comparison result curve of the whole test of the normal distribution estimation result, by using the third party analysis and recording software, the weighted analysis shows that the overall result of the normal distribution parameter estimation method designed in this paper is 78.42%.The result of the traditi nal normal distribution parameter estimation method is 37.68%.


Error rate test analysis of normal distribution estimation results

At the same time, the error rate test of normal distribution estimation resul

was conducted for d
fferent carbonization types and different normal distribution parameter estimation methods.The error rate test results comparison curve of the normal distribution estimation results is shown in Fig. 7.

According to the results of normal distribution estimation error rate comparison test curve, using the weighted analysis by the third-party analysis and recording software makes the error rate of the normal distribution parameter estimation method designed in this paper at 9.8%, while the traditional normal error rate in the distribution parameter estimation method is 23.7%.


Normal distribution parameter hit ratio calculation

The error rate of normal distribution estimation results and normal distribution estimation results w re substituted into the normal distribution parameter hit ratio calculation formula.Its normal distribution parameter hit ratio calculation formula is as follows:
χ ¼ 1 n P n i¼1 ðC i −KQ i Þ; (32)
in which C represents the integrity test results of the Gaussian distrib tion estimation.Q represents the normal distribution estimation result error rate.K represents the simulation coefficient of the test and is taken 0.98 in the paper.n represents trial stretch and is taken 400.

The method that is put forward is named χ 1 and the normal method is called χ 2 .If Δχ = χ 1 − χ 2 is a positive number, it represents a decrease in risk management.If Δχ = χ 1 − χ 2 is a negative, it represents the risk reduction.Δχ can be taken in equation 32.Compared with the conventional parameter estimation method, the proposed parameter estimation method increases the hit ratio by 22.12%, which is suitable for the normal distribution parameter estimation of carbon

ation depth.
Δχ ¼ χ 1 −χ 2 ¼ 1 n X n i¼1 C 1i −KQ 1i ð Þ
− 1 n X n i¼1 C 2i −KQ 2i ð Þ ¼ 0:221209

Conclusion

Normal distribution is proposed in this paper to determine carbonation depth deviation estimation.Based on the construction of the normal distribution parameter estimation model and the normal distribution parameter of the linear deviation calculation, the result is determined with the maximum similarity value of the parameter.The normal distribution parameter estimation of carbonization depth is realized by using the Bayesian function of carbonization depth.The experimental data show that the proposed method is highly effective.It is hoped that this study can provide theoretical basis for the normal distribution parameter estimation method of carbonization

pth.



Cai and Yang EURASIP Journal on Wireless Communications and
Networking (2020) 2020:86 curve will get an inflection point when x equals to μ ± σ.It can be seen from Fig. 2 that the distribution function is a smooth S curve and a normal distribution function graph.When σ = 0.5, μ takes different values on the normal distribution graph, as shown in Fig. 3.The size of fixed parameters can be seen, changing the parameters of average as well as the graphic translation but not change its shape along the x axis, showing the position of probability density function of the normal distribution.When σ = 0, μ takes different values on the normal distribution graph, as shown in Fig. 4. When μ has fixed average parameters, changing the scale param

er sigma, normal distribution probability density fu
ction of the basic position and shape remains unchanged, only on the longitudinal tensile and compression effect.It is a little bit flat as it gets smaller and smaller.


Fig. 1 Fig. 2
12
Fig. 1 Probability density function graph of normal distribution


Fig. 3
3
Fi h a minimum of 0.5 difference




Cai and Yang EURASIP Journal on Wireless Communications and Networking (2020) 2020:86




thermore, if we want to estimate the function of θ 1 , θ 2 , …, θ k , η = g(θ 1 , θ 2 , …, θ k ) will give a direct estimate as shown in equation 11.


Fig. 5
5
F g. 5 Schematic curves of normal distribution parameters




Cai and Yang EURASIP Journal on Wireless Communications and Networking (2020) 2020:86


Fig. 7
7
Fig. 7 Error rate test comparison curve of normal distribution estimation results




AbbreviationDE: Decision element




Cai and Yang EURASIP Journal on Wireless Communications and Networking (2020) 2020:86

[13,14]he following condition Lð θÞ ¼ max θ∈Θ LðθÞ (1.11), θ is called the Maximum Likelihood Estimation of θ, which is abbrev