CRCcheck

说明：  这是个关于软件方程的运行代码，很简单，有需要的朋友可以下载看下，对你或许有帮助。(This is the operation code of the software equation is very simple, a friend in need can be downloaded to look, you may have to help.)

利用小波分解得到各尺度上的信号，再积分求取各尺度上的能量，可用于故障诊断(Signal using wavelet decomposition on each scale, and then strike points on each scale energy can be used for troubleshooting)

用于提取语音每帧的mfcc函数，将数据保存在cell文件中，可以直接使用cell，也可经过变换，提取到表格中使用(The MFCC function used to extract each frame of speech is saved in cell file. Cell can be used directly or transformed to extract the data into tables)

用于网络服务的matlab源程序，没有解压密码，可直接打开使用(network services for the Matlab source, without extracting passwords, can be directly used to open)

主成分分析离线功能实现和用法，这里是源代码，代码详细，数据齐全(Principal component analysis offline implementation and usage, here is the source code, the code in detail, the data is complete)

现代信号处理中谱估计在matlab中的使用，tuhrzmX参数单径或多径瑞利衰落信道仿真，双向PCS控制仿真，BP神经网络用于函数拟合与模式识别，hCxHWym条件包括主成分分析、因子分析、贝叶斯分析，使用混沌与分形分析的例程。( Modern signal processing used in the spectral estimation in matlab, tuhrzmX parameter Single path or multipath Rayleigh fading channel simulation, Two-way PCS control simulation, BP neural network function fitting and pattern recognition, hCxHWym condition Including principal component analysis, factor analysis, Bayesian analysis, Use Chaos and fractal analysis routines.)

说明：  考虑不确定性的配电网经济调度问题，用列与约束算法解决。(Considering the uncertain economic dispatching problem of distribution network, the algorithm of column and constraint is used to solve the problem.)

基于matlab的直接序列扩频通信系统仿真(Simulation of direct sequence spread spectrum communication system based on MATLAB)

基于MATLAB6.0实现的体彩双色球预测算法，基础原理是rbp神经网络(Based on MATLAB6.0 implementation Ticai Shuangse Qiu prediction algorithm, neural network-based principle is rbp)

这是一个经典的形变模型实施,在一个单一的文件用简单的可以理解的代码。 功能包括两部分一个简单的弱分类器和一个促进部分: 弱分类器试图找到最佳阈值的数据维数对数据进行分离成两个阶级1和1 要求的进一步提高分类器部分迭代,每一步是变化分类权重miss-classified例子。这造成了一连串的“弱分类器”,表现得像一个“强大分类器” (This a classic AdaBoost implementation, in one single file with easy understandable code. The function consist of two parts a simple weak classifier and a boosting part: The weak classifier tries to find the best threshold in one of the data dimensions to separate the data into two classes-1 and 1 The boosting part calls the classifier iteratively, after every classification step it changes the weights of miss-classified examples. This creates a cascade of "weak classifiers" which behaves like a "strong classifier" . )