搜索资源列表
KMEANS(matlab)
- Matlab环境下的k-means聚类算法，实现图像分割，很快阿！-K-means Clustering arithmetic based on Matlab platform.It s fast for Image-Division!
K-means.m
- MATLAB编写的一种模式识别里的重要的模糊聚类方法K-means算法的matlab程序.-MATLAB prepare a Pattern Recognition's important Fuzzy clustering methods K-means algorithm Matlab procedures.
stprtool.rar
- 统计模式识别工具箱（Statistical Pattern Recognition Toolbox）包含： 1，Analysis of linear discriminant function 2，Feature extraction: Linear Discriminant Analysis 3，Probability distribution estimation and clustering 4，Support Vector and other Kernel Machines,
clutter.rar
- 基于Matlab的 GUI的集成软件，功能包括： 1、海杂波模型仿真和统计分析（瑞利、韦伯、对数和K分布SIRP和ZMNL法）； 2、海杂波/波导参数估计（海杂波、多径和波导参数和雷达探测性能估计）； 3、实测海杂波数据统计分析。,Matlab' s GUI-based integrated software, features include: 1, sea clutter model simulation and statistical analysis (Rayleigh,
lle_roweis.rar
- 这是LLE的原始算法，原文的参考文献是：S.T.Roweis and L.K.Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290, 2000.,This is the original LLE algorithm, the original reference is: STRoweis and LKSaul. Nonlinear dimensionality reduction b
CodeBook
- Code MATLAB in S. Theodoridis, A. Pikrakis, K. Koutroumbas, D. Cavouras - Introduction to Pattern Recognition: A MATLAB Approach
simulation
- 设计一个M/M/S/k排队系统模型，用C++进行仿真，计算一个新的服务请求在各个状态k下的阻塞率，绘制出到达率-阻塞率曲线(matlab)，并将理论值与仿真值进行比较。 M/M/S/k模型为服务请求到达间隔时间服从泊松分布、服务时间服从指数分布、系统有S个服务器、系统容量为k（有限个）的排队系统。如果一个服务请求到达排队系统时，系统内已有k个服务请求，那么这个服务请求就会被拒绝（即不为该服务请求安排服务器，也不会将其排入系统队列）。由于系统容量为k，有S个服务器，所以系统队列的长度为k-S
KolmogorovEntropy_GP
- 一种简单有效的测度熵替代方法——近似熵(approximate entropy)方 法.应用以上方法对Logistic映射复杂度进行了分析.结果表明Lyapunov指数和测度熵的值与复 杂度基本呈线性关系,分维数与复杂度的函数关系尚难确定,且与Lyapunov指数、测度熵之间的关 系也不明确. - Approximate entropy (ApEn) method is also studied. They are applied to analyze the complex
Discrete_Cosine_and_Sine_Transforms
- 继 Discrete Cosine Transform: Algorithms, Advantages, Applications (K. R. Rao and P. Yip) 之后的一本关于离散余弦变换(DCT)的快速实现算法的最新专著. 本书第五章“Integer Discrete Cosine/Sine Transforms”关于DCT整数近似实现写的非常详细和精彩, 很方便大家编程实现. 值得一提的是本书的第一作者Vladimir Britanak 一直在从事DCT, 尤其是改进的离散余
EmdL1_v2
- NOTES: 1. This implemention borrowed some basic framework from Rabner s original EMD code. 2. Histogram matrices are assumed to be arranged in the "matlab" style, i.e, the [i,j,k]-th element is located in the position i*(n2*n3) + j*(n3
SENSEgfactorcalculation
- 计算SENSE重建图像中的g-factor,这是并行磁共振成像SENSE算法的关键一步-G-factor is the metric to quantify the amplificaiton of noise power in reconstructing SENSE accelerated image. The detail was presented in Pruessmann s 1999 Magn. Reson. Med. paper. In theory, g-factor is t
chapter2
- 数字信号处理及其matlab实现 （美） Vinay k.ingle john g.proakis著 第二章-Digital signal processing and matlab implementation (U.S.) Vinay k.ingle john g.proakis the second chapter
m
- 该程序试图考察一组数据服从哪种分布（正态，指数或双边指数），并利用K-S检验对各种分布作了检验。-The program attempts to examine a set of data subject to which distribution (normal, exponential or bilateral index), and use KS test has been tested in a variety of distributions.
Signals_Systems_MATLAB
- Won Y. Yang · Tae G. Chang · Ik H. Song · Yong S. Cho · Jun Heo · Won G. Jeon · Jeong W. Lee · Jae K. Kim Signals and Systems with MATLAB
ARX
- we have a photo camera with DC Motor and closed loop controller is equal k/s^2+s+k. first,we shoul find ZOH,then we identify the system with ARX model that na=nb=2 Here,we want to identify the Dynamic Linear System. we use different inputs such
proj10-01
- 在试验中编写程序实现了K均值聚类算法,K均值聚类的原理是：在训练样本中找到C个聚类中心，每个聚类中心代表一个类的中心。然后将样本归类到与其最近的聚类中心的那一类。 C的选择是通过先验知识或经验选取的。聚类中心是通过算法迭代求得的。-In the test preparation process to achieve a K means clustering algorithm, K means clustering principle is: in the training samples to
the-four-step-ranger-r-k
- 四阶经典R-K方法的Matlab程序和newton的matlab程序的实现-Matlab program to achieve the classic fourth-order RK method and newton s matlab program
KOLMOGOROV_TEST
- 基于matlab的K-S test算法实现，可供初学者参考和学习。(K-S test algorithm based Matlab, for beginners reference and learning.)
matlab simulation for CDMA
- Chapter 18 README FILE Prepared by: William H. Tranter Department of Electrical and Computer Engineering Virginia Tech - Mail Code 0350 Blacksburg, VA 24061 email: btranter@vt.edu Revision Dates: June 20, 2004 Note: This readme f