The Method of Optimal Group Based on the Kernel Density Estimation and the Close Degree

2014 ◽  
Vol 989-994 ◽  
pp. 3689-3692
Author(s):  
Fei Ye ◽  
Xin Wang ◽  
Dong Hui Peng ◽  
Chuan Hai Jiao
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Algorithm ◽  
Parameter Analysis ◽  
Sample Distribution ◽  
Group Number ◽  
Selection Of

The optimal group is an important problem of histogram algorithm, and how to confirm group number has not a quantitative rule. So the concept of the close degree is imported to make the close degree between the upper contour line of histogram and the PDF(probability density function) of parameter as the judging criteria of optimal group. With the unknown of the PDF of parameter, the improved kernel density estimation algorithm can pre-select and estimate the PDF. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the window's width automatic adjustment value between the different estimation points based on the sample distribution. In the parameter analysis of radar emitter signal, the algorithm based on improved kernel density estimation and close degree is used to determine optimal group, and the result indicate that this method is effective and can search the optimal group automatically.

The Feature Description Method of Radar Signal Based on Improved Kernel Density Estimation

2013 ◽  
Vol 380-384 ◽  
pp. 3501-3504
Author(s):  
Fei Ye ◽  
Jie Zhou ◽  
Jun Luo ◽  
Xing Rong Gao
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Estimation Algorithm ◽  
Radar Signal ◽  
Sample Distribution ◽  
Feature Description ◽  
Feature Based ◽  
Selection Of ◽  
Probability Density Curve

According to the problem that the existing radar signal feature cannot effectively express and analysis its characteristic, a description method of radar emitter signal feature based on improved kernel density estimation is proposed. This improved kernel density estimation algorithm combine the selection of fixed window and variable window's width to achieve the window's width automatic adjustment value between the different estimation points based on the sample distribution. Then the probability density curve using kernel density estimation algorithm as radar emitter signal parameters characteristics stored into database.

A Statistical Model for Wind Power Forecast Error Based on Kernel Density Estimation

2014 ◽  
Vol 8 (1) ◽  
pp. 501-507
Author(s):  
Liyang Liu ◽  
Junji Wu ◽  
Shaoliang Meng
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Wind Power ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Forecast Error ◽  
Kernel Density ◽  
Wind Power Forecast ◽  
Proposed Model ◽  
Chi Squared

Wind power has been developed rapidly as a clean energy in recent years. The forecast error of wind power, however, makes it difficult to use wind power effectively. In some former statistical models, the forecast error was usually assumed to be a Gaussian distribution, which had proven to be unreliable after a statistical analysis. In this paper, a more suitable probability density function for wind power forecast error based on kernel density estimation was proposed. The proposed model is a non-parametric statistical algorithm and can directly obtain the probability density function from the error data, which do not need to make any assumptions. This paper also presented an optimal bandwidth algorithm for kernel density estimation by using particle swarm optimization, and employed a Chi-squared test to validate the model. Compared with Gaussian distribution and Beta distribution, the mean squared error and Chi-squared test show that the proposed model is more effective and reliable.

scholarly journals Kernel density estimation and its application

2018 ◽  
Vol 23 ◽  
pp. 00037 ◽  
Author(s):  
Stanisław Węglarczyk
Keyword(s):  
Probability Density Function ◽  
Probability Distribution ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Real Life ◽  
Kernel Estimation ◽  
Kernel Density ◽  
Two Dimensional ◽  
2D Histogram ◽  
Function Shape

Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel technique produces smooth estimate of the pdf, uses all sample points' locations and more convincingly suggest multimodality. In its two-dimensional applications, kernel estimation is even better as the 2D histogram requires additionally to define the orientation of 2D bins. Two concepts play fundamental role in kernel estimation: kernel function shape and coefficient of smoothness, of which the latter is crucial to the method. Several real-life examples, both for univariate and bivariate applications, are shown.

scholarly journals Benchmarking the reliability of medium-voltage lines

2017 ◽  
Vol 68 (3) ◽  
pp. 212-215 ◽  
Author(s):  
Michal Kolcun ◽  
Mirosław Kornatka ◽  
Anna Gawlak ◽  
Zsolt Čonka
Keyword(s):  
Density Distribution ◽  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Kernel Density ◽  
Power Lines ◽  
Probability Density Distribution ◽  
Nonparametric Method ◽  
Medium Voltage

Abstract This paper discusses the problem of reliability of the Polish medium voltage power lines. It presents the benchmarking of power lines and values of SAIDI. The probability density distribution of the medium voltage lines length as well as selected indicators of the MV power lines reliability, being operated by the distribution companies under test, are also introduced. The analysis is based on the actual MV lines and uses the nonparametric method of kernel density estimation.

scholarly journals Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques

2019 ◽  
Vol 11 (24) ◽  
pp. 6954
Author(s):  
Fuqiang Li ◽  
Shiying Zhang ◽  
Wenxuan Li ◽  
Wei Zhao ◽  
Bingkang Li ◽  
...  
Keyword(s):  
Probability Density ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Clustering Algorithm ◽  
Estimation Method ◽  
Kernel Density ◽  
Support Vector ◽  
Average Width ◽  
Power Load ◽  
Density Forecasting

In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrates K-means clustering algorithm, Salp Swarm Algorithm (SSA), Least Square Support Vector Machine (LSSVM), and kernel density estimation (KDE) method. Firstly, the loads at 24 times a day are grouped into three categories according to the K-means clustering algorithm, which correspond to the valley period, flat period, and peak period of the load, respectively. Secondly, the load point forecasting value is obtained by LSSVM method optimized by SSA algorithm. Furthermore, the kernel density estimation method is employed to fit the forecasting error of SSA-LSSVM in different time periods, and the probability density function of the error distribution is obtained. The final load probability density forecasting result is obtained by combining the point forecasting value and the error fitting result, and then the upper and lower limits of the confidence interval under the given confidence level are solved. In this paper, the performance of the model is evaluated by two indicators named interval coverage and interval average width. Meanwhile, in comparison with several other models, it can be concluded that the proposed model can effectively improve the forecasting effect.

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