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最小二乘递推算法的Matlab仿真
v(k)是服从N(0,1)分布的不相关随机噪声-recursive least squares algorithm Matlab v (k) is subject N (0, 1) not related to the distribution of the random noise
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ofdm系统中功率自适应分配算法研究的matlab源代码（k＝20）-ofdm Power System Adaptive Algorithm for the distribution of the source code Matlab (k = 20)
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％ PIEFLAB Main Directory
％ ----------------------
％
％ .m - files
％ ----------
％ Contents.m : this file
％ startup.m : startup file: sets Matlab path executed automatically when
％ Matlab command is performed in this directory
％
％ s
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利用matlab统计图中结点度的分布以及节点都发生频率的分布。结果出现两张图，一个是统计图中各个结点度k的分布，一张是统计网络结点度发生频率即为概率P(k)的分布图。-the source code can statistics the degree distribution of graph with matlab. it is very useful to the analysis of complex network study.
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海杂波K-分布模型模拟仿真程序
海杂波K-分布模型模拟仿真程序-K distribution in matlab
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海杂波服从K分布，K杂波在matlab中仿真，分析雷达性能-Sea clutter obeys K distribution, K clutter in matlab simulation, analysis of radar performance
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产生k分布，瑞利分布，正太分布，PSK_lfm,RAY,等信号的matlab文件-Generated k distribution, Rayleigh distribution, normally distributed, PSK_lfm ray signals such as Matlab files
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k-d树的matlab程序，包含了KDTREE, KDTREEIDX and KDRANGEQUERY functions。提供了两个Demo
scr ipts(kdtree_demo.m & kdrange_demo.m),很实用-
This distribution contains the KDTREE, KDTREEIDX and KDRANGEQUERY functions.
Included are the source code files in kdtree/s
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用函数[z，p，K]=tf2zp（num，den）求得有理分式形式的系统转移函数的零、极点，用函数zplane（z，p）绘出零、极点分布图；-Form of rational fraction obtained with the function [z, p, K] = tf2zp (num, den) system transfer function zero poles, with the function zplane (z, p) draw zero, pole distribution
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利用电压电流泰勒展开，计算极限潮流分布，也可计算不同负荷情况下的潮流分布，只要改动负荷因数K的大小即可-Taylor voltage and current use to calculate the limit flow distribution, flow distribution can also be calculated under different load conditions, as long as the changes in the size of the load fact
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K-分布的Matlab程序示意图，用于观察K-分布的图形特性-Matlab program distribution diagram of K-, K- distribution observed for graphics features
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用matlab仿真k分布雷达杂波，并进行处理-K-distribution radar clutter processing
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本实验的目的是学习Parzen窗估计和k最近邻估计方法。在之前的模式识别研究中，我们假设概率密度函数的参数形式已知，即判别函数J(.)的参数是已知的。本节使用非参数化的方法来处理任意形式的概率分布而不必事先考虑概率密度的参数形式。在模式识别中有躲在令人感兴趣的非参数化方法，Parzen窗估计和k最近邻估计就是两种经典的估计法。(The purpose of this experiment is to study the Parzen window estimation and the k nea
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