当前位置:
首页 资源下载
搜索资源 - Density Estimation
搜索资源列表
-
0下载:
Abstract: Randomized modulation is of increasing interest in power
electronics and also holds promise for reducing filtering requirements
in ac/ac, dc/dc converter application and reducing acoustic noise in
motor drive applications. This paper
-
-
2下载:
这是几个不同形状参数的广义高斯分布的密度函数作图,以及形状参数的极大似然估计的程序。-This is several different shape parameters of the generalized Gaussian distribution density function mapping, as well as the shape parameter of the great likelihood estimation procedures.
-
-
1下载:
用Parzen窗法或者kn近邻法估计概率密度函数,得出贝叶斯分类器,对测试样本进行测试,比较与参数估计基础上得到的分类器和分类性能的差别.2. 同时采用身高和体重数据作为特征,用Fisher线性判别方法求分类器,将该分类器应用到训练和测试样本,考察训练和测试错误情况。将训练样本和求得的决策边界画到图上,同时把以往用Bayes方法求得的分类器也画到图上,比较结果的异同。3.选择上述或以前实验的任意一种方法,用留一法在训练集上估计错误率,与在测试集上得到的错误率进行比较。-Use Parzen Wi
-
-
2下载:
用matlab进行概率密度函数的非参数估计,主要有parzen窗法和kn近邻法。分别对平均分布和正态分布进行了仿真。-Non-parametric estimation of the probability density function using matlab, main the parzen window method and kn nearest neighbor method. The average distribution and normal distribution were
-
-
0下载:
estimation of density probability by histogramme
-
-
0下载:
用监督参数估计中的贝叶斯方法估计条件概率密度的参数u-Using the supervision parameter estimation in Bayesian method estimates the parameters of the conditional probability density u
-
-
0下载:
A new and efficient algorithm for high-density salt
and pepper noise removal in images and videos is proposed.The existing non-linear filter like Standard Median Filter
(SMF), Adaptive Median Filter (AMF), Decision Based
Algorithm (DBA) and Rob
-
-
0下载:
A blind source separation algorithm is proposed
that is based on minimizing Renyi’s mutual information by means
of nonparametric probability density function (PDF) estimation.
The two-stqge process consists of spatial whitening
-
-
2下载:
给定数据和相应的概率密度函数、用matlab求解其相应的极大似然估计-Given data and the corresponding probability density function using matlab to solve the corresponding maximum likelihood estimation
-
-
1下载:
支持向量机在线学习训练算法C++源码,可以用来做数据分类、模式识别、回归估计、概率密度函数估计等方面。-Support vector machine online learning training algorithm C++ source code, can be used for data classification, pattern recognition, regression estimation, probability density function estimation an
-
-
0下载:
周期图方法频率估计
傅里叶变换求功率谱密度
用Matlab编程仿真实现-Periodogram method of frequency estimation Fourier transform for power spectral density using Matlab programming simulation
-
-
0下载:
粒子滤波源代码,通过寻找一组在状态空间中传播的随机样本来近似的表示概率密度函数,用样本均值代替积分运算,进而获得系统状态的最小方差估计的过程。-Particle filter source code, by finding a set of transmission in the state space representation of a random sample to approximate the probability density function, instead of usi
-
-
0下载:
Estimation of Tool Pose Based on Force–Density
Correlation During Robotic Drilling
-
-
0下载:
研究表明超高斯分布更加贴近语音信号的实际分布,然而语音信号很难用单一的概率密度
函数准确描述,针对这一情况,提出了一种用超高斯混合模型对语音信号幅度谱建模的新方法,并推导了
基于此模型的幅度谱最小均方误差估的估计式。仿真结果表明:与传统的短时谱估计算法相比,该算法不
仅能够进一步提高增强语音的信噪比,而且可以有效减小增强语音的失真度,提高增强语音的主观感知
质量。 -Recent research indicates that the speech spectral ampli
-
-
1下载:
ex6_1 ~ ex6_3二项分布的随机数据的产生
ex6_4 ~ ex6_6通用函数计算概率密度函数值
ex6_7 ~ ex6_20常见分布的密度函数
ex6_21 ~ ex6_33随机变量的数字特征
ex6_34 采用periodogram函数来计算功率谱
ex6_35 利用FFT直接法计算上面噪声信号的功率谱
ex6_36 利用间接法重新计算上例中噪声信号的功率谱
ex6_37 采用tfe函数来进行系统的辨识,并与理想结果进行比较
ex6_38 在置信度为0
-
-
0下载:
MATLAB实现最大似然估计,本程序可以实现概率密度函数已知的情况下5个参数的估计~-MATLAB to achieve maximum likelihood estimation, the program can be achieved under the circumstances known to the probability density function estimation parameters 5 ~
-
-
0下载:
利用粒子滤波通过融合颜色信息和运动信息来计算粒子权值法,适用于任何分布的状态估计问题,是用一些离散随机采样点来近似系统随机变量的概率密度函数-Particle filter information through the integration of color and motion information to calculate particle weights method for estimation of the distribution of any state, with a nu
-
-
0下载:
K近邻(KNN):分类算法KNN是non-parametric分类器(不做分布形式的假设,直接从数据估计概率密度),是memory-based learning KNN不适用于高维数据(curse of dimension)-K-Nearest Neighbor (KNN): Classification Algorithm. KNN is a non-parametric classifiers (not to assume that the distribution of forms, fr
-
-
0下载:
FFT 概率密度函数估计 概率论与数理统计-FFT probability density function estimation
-
-
1下载:
视频图像的人群密度检测,多种人群密度场景下人群计数算法:
算法功能:建立图像特征和图像人数的数学关系
算法输入:训练样本图像1,2…K
算法输出:模型估计参数 ,参考图像
算法流程:1)对训练样本图像进行分块处理(算法1.1);
2)通过算法1.2,计算训练样本各个对应分块的ALBP特征归一化,再用K-means算法(可使用opencv等算法库实现,不再描述其算法),将图像块分成k(k<K)类,获取k(k<K)个聚类中心,即为参考图像;
3)对分块的图像进行与
-