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system-identification
- 系统辨识,采用最小二乘法,运用随机数据,高斯白噪声数据等来辨识-Identification, using the least squares method, using random data, Gaussian white noise and other data to identify
P84li3.1
- 通过给出的随机过程,该随机过程是由3个实正弦信号组成,归一化频率分别为0.1,0.25,0.27,其中相位是相互独立在【0,2*pi】上分布的随机相位;噪声是均值为0、方差为1的实高斯白噪声,信噪比为30db,30db,27db,观测样本数为N,通过bartlett法,Welch法,周期图法计算随机过程的概率谱-Given by stochastic process, the stochastic process is composed of three real sine signal, th
add_noise
- 函数用以产生高斯噪声,瑞利噪声,伽马噪声等噪声的随机序列-Function for generating random sequences of Gaussian noise, Rayleigh noise, noise, noise, gamma
filter-in-video-sequences
- 粒子滤波理论是近年来跟踪领域的热门研究课题。在该领域,传统的卡尔曼(Kalman)滤波器是非常经典的运动目标跟踪工具。然而经典亦有其弊端,卡尔曼滤波对于非线性及非高斯环境下的工作能力相当无力。为解决这一问题,本文提出了一种基于粒子滤波的目标跟踪方法。其核心为以粒子(一种随机样本,携带权值)来表示后验概率密度,从而得到基于物理模型的近似最优数值解,其优点在于能在追踪的过程中实现更高的精度和更快的收敛速度等。粒子滤波通过加权计算这些带有权重的随机样本来得到目标的近似的运动状态,因此对于非高斯和非线性
hingfai_V8.8
- 高斯白噪声的生成程序,快速扩展随机生成树算法,部分实现了追踪测速迭代松弛算法。- Gaussian white noise generator, Rapid expansion of random spanning tree algorithm, Partially achieved tracking speed iterative relaxation algorithm.
benhang
- 高斯白噪声的生成程序,粒子图像分割及匹配均为自行编制的子例程,快速扩展随机生成树算法。- Gaussian white noise generator, Particle image segmentation and matching subroutines themselves are prepared, Rapid expansion of random spanning tree algorithm.
maohei
- 最小均方误差(MMSE)的算法,高斯白噪声的生成程序,快速扩展随机生成树算法。- Minimum mean square error (MMSE) algorithm, Gaussian white noise generator, Rapid expansion of random spanning tree algorithm.
quiban_V4.1
- 实现典型相关分析,高斯白噪声的生成程序,包括随机梯度算法,相对梯度算法。- Achieve canonical correlation analysis, Gaussian white noise generator, Including stochastic gradient algorithm, the relative gradient algorithm.
ml_least_square_method_p030
- 使用随机梯度算法对高斯核模型进行最小二乘法学习-The Gaussian kernel model is learned by least squares method using stochastic gradient algorithm
GaussianRoughSurfaceGeneration_2D
- 二维随机粗糙面生成,采用高斯谱,模特卡罗方法-gaussion 2d
ban_ya70
- 一个师兄的毕设,高斯白噪声的生成程序,随机调制信号下的模拟ppm。- A complete set of brothers, Gaussian white noise generator, Random ppm modulated analog signal unde.
GUSS
- 多点高斯平稳随机过程模拟,运用了快速傅里叶变换进行模拟。-Multi-Gaussian stationary random process simulation, using a fast Fourier transform simulation.
Optimalreceptionofadditivewhite
- 本设计主要对4PSK调制方式的信号,利用MATLAB的m文件进行最佳接收机的设计与仿真。对输入的叠加噪声的4PSK调制信号进行接收,利用相关解调器来实现信号解调,及最大似然准则来实现检测器。在相关解调器中,接收信号分别与基函数 和 相乘再积分。在检测器中,利用相位来判断输出,从而最终得到接收的数据。采用随机二进制数通过4PSK调制后叠加高斯白噪声再对设计的接收机进行测试,从测试的结果可看出,在信噪比大于-8dB时,误码率为0,说明该接收机较好的实现了抗噪声性能。-The design of th
369031
- 对模糊变量进行随机化,并用高斯分布进行概率求解,无密码-To randomization of fuzzy variables, and probability to solve with the gaussian distribution, no password
045237
- 对模糊变量进行随机化,并用高斯分布进行概率求解,无密码-To randomization of fuzzy variables, and probability to solve with the gaussian distribution, no password
lox
- 对模糊变量进行随机化,并用高斯分布进行概率求解,无密码-To randomization of fuzzy variables, and probability to solve with the gaussian distribution, no password
nyfgtoq
- 对模糊变量进行随机化,并用高斯分布进行概率求解,无密码-To randomization of fuzzy variables, and probability to solve with the gaussian distribution, no password
新建 Microsoft Word 文档
- 现有某高度的测量误差均值为0,方差为1的高斯白噪声随机序列,通过卡尔曼滤波原理求该物体的初始高度和速度。(A Gauss white noise random sequence with a mean measurement error of 0 and a variance of 1 is obtained. The initial height and velocity of the object are calculated by the Calman filtering principl
5372323
- 对模糊变量进行随机化,并用高斯分布进行概率求解,无密码(To randomization of fuzzy variables, and probability to solve with the gaussian distribution, no password)
高风代码
- 本内容是有关机器学习的包含贝叶斯分类器,随机森林,支持向量机,神经网络,logistic多元回归等(The contents of this paper are machine learning, including Bayesian classifier, random forest, support vector machines, neural network, logistic multiple regression and so on)