glmlab

本程序实现了找出一个n个数的序列中最长的单调递增子序列,并符带测试数据(This program implements to find a sequence of n the number of the longest monotonically increasing subsequence, and to break with the test data)

本文件为基于OMNeT_的Leach协议的仿真改进与研究(This document is based on the improvement and simulation of Leach agreement OMNeT_)

最基本的DOA算法--Capon算法的matlab仿真代码，希望对你有用！(Capon algorithm of matlab code for DOA. Hope it ll be a little useful for you!)

good for understanding

Track Content Provider implements I Structured Content Provider.

目标因子分析，用于找寻主因子数的个数，用途广泛(Target factor analysis, to find the main number for the number of factors, versatile)

给定一批的样本，当然这些样本间必须相互独立。如果Q > chi2(p, nu)，假设拒绝，否则通过。 ARGUMENTS: x Absolut numbers. p The prob ability value, calculated from Q. Q The resulting Q-value.(A given batch of samples, of course, among these samples must be independent of each other. If Q> chi2 (p, nu), the assumption that refuse, or else through. ARGUMENTS: x Absolut numbers. P The prob ability value, calculated from Q. Q The resulting Q-value.)

说明：  Language 求已知数据点的拉格朗日插值多项式 Atken 求已知数据点的艾特肯插值多项式 Newton 求已知数据点的均差形式的牛顿插值多项式 Newtonforward 求已知数据点的前向牛顿差分插值多项式 Newtonback 求已知数据点的后向牛顿差分插值多项式 Gauss 求已知数据点的高斯插值多项式 Hermite 求已知数据点的埃尔米特插值多项式 SubHermite 求已知数据点的分段三次埃尔米特插值多项式及其插值点处的值 SecSample 求已知数据点的二次样条插值多项式及其插值点处的值 ThrSample1 求已知数据点的第一类三次样条插值多项式及其插值点处的值 ThrSample2 求已知数据点的第二类三次样条插值多项式及其插值点处的值 ThrSample3 求已知数据点的第三类三次样条插值多项式及其插值点处的值 BSample 求已知数据点的第一类B样条的插值 DCS 用倒差商算法求已知数据点的有理分式形式的插值分式 Neville 用Neville算法求已知数据点的有理分式形式的插值分式 FCZ 用倒差商算法求已知数据点的有理分式形式的插值分式 DL 用双线性插值求已知点的插值 DTL 用二元三点拉格朗日插值求已知点的插值 DH 用分片双三次埃尔米特插值求插值点的z坐标 (chazhi)

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle s position and velocity. Each particle s movement is influenced by its local best known position and is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.

适用于水声通信系统中，程序中是在多径条件下， 均衡（LMS）(underwater acoustic communication)