exzample

这是神经模糊网络中的D-FNN，极其所用到的函数程序。(This is a neuro-fuzzy network in the D-FNN, most of the procedures used in the function.)

缺失部分方位信息的辐射计阵列有源校正 (Radiometer array azimuth information of the missing part of the active correction)

说明：  matlab计算皮尔逊相关系数、斯皮尔曼相关系数、正态分布检验等。(The Pearson correlation coefficient, Spearman correlation coefficient and normal distribution test were calculated by MATLAB.)

QPSK的调制解调系统的实现，程序详细给出了调制解调各个部分的方法。自己在matlab上实现过，都出来了(QPSK modulation and demodulation system implementation, the program details the various parts of the modulation and demodulation method. Their implementation in matlab too, are out)

基于相位相关和基于尺度不变特征点的图像拼接算法matlab源程序代码(phase correlation and feature point scale based on Invariant scale image stitching algorithm matlab source code)

通达信上的指标，明确显示支撑与压力的价格，并给予买卖和止损的信号。(Mastery indicators on the letter, clearly shows the price support and pressure, and the sale and stop signals given.)

输电线路故障定位，含有几种不同线路情况，两端，三端，多端。并配有行波提取程序(Transmission line fault location, with several different line conditions, both ends, three ends, multi-terminal. And with a traveling wave extraction program)

Plots time and frequency domains of UWB PPM monocycles and doublets with basic correlation receiver.

Stephen Boyd编写的凸优化理论书籍，学习凸优化方法必备书籍(Convex Optimization By Stephen Boyd)

现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。(existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.)