fdtd

基于粒子群的神经网络优化算法的应用，在土壤水分特征曲线中的应用。(Neural network based on particle swarm optimization algorithm applied in the soil moisture characteristic curve application.)

可以用于预测的小波分解下的多元线性回归,对于径流预测有效(wavelet mlr)

it is a program to identify the persons using his own iris

所传代码主要功能是通过MCKD算法对故障信号进行故障特征频率的提取(The main function of the code is to extract the fault characteristic frequency of the fault signal by MCKD algorithm)

说明：  OFDM的FIND PACKET仿真，是用Matlab编程实现的(OFDM FIND PACKET simulation, using the Matlab programming)

说明：  数字通信16QAM，16QPSK的实现思路及分析，报告详细，有误码率的分析(Digital communication 16QAM, 16QPSK implementation ideas and analysis, report details, there is the analysis of bit error rate)

this is random decermin technique for mode estimation with wavelet techiniq

说明：  用Newton插值多项式和三次样条插值多项式，已知f(x)=1/(1+25x^2)(With the Newton interpolation polynomial and cubic spline interpolation polynomial, known f (x) = 1/(1+25 x ^ 2))

直接序列扩频（DS）原理的仿真程序，通过该程序你可以很好地理解直接序列扩频原理。(Direct Sequence Spread Spectrum (DS) The principle of simulation program, through the program you can very well understand the principle of direct sequence spread spectrum.)

matlab 关于association rule 的自己写的函数，有3个文件, association.m：h = association(m, i, j) i=>j, m是数据，h是support和confidence，该函数只适用于单个数据 ass_item: h=ass_itset(m, a, b) 同上，但是可用于多个数据（m为数组） assrule: h = assrule(m, threshold1, threshold2) 该函数用于classification， 得到规则，threshold1为要求的support,threshold2为要求的confidence，h 则为符合要求的规则及其support和confidence，前2列为规则，后2列为其support和confidence (matlab on the association rule to write functions, there are 3 files, association.m: h = association (m, i, j) i => j, m is the data, h is the support and confidence, this function applies only to a single Data ass_item: h = ass_itset (m, a, b) it is the same as above, but it can be used for multiple data (m can be matrix) assrule: h = assrule (m, threshold1, threshold2) the function used for classification,get the rules, threshold1 is the require of support, threshold2 is the required of confidence, h is the rules and their support and confidence, the former two columns as a rule, the latter two columns as one of its support and confidence)