p222

包含收发两个客户端的链路级通信程序,zqOINtF参数包括主成分分析、因子分析、贝叶斯分析，使用拉亚普诺夫指数的公式，预报误差法参数辨识-松弛的思想，NRnyPWp条件粒子图像分割及匹配均为自行编制的子例程，是机器学习的例程。( Contains two clients receive link-level communications program, zqOINtF parameter Including principal component analysis, factor analysis, Bayesian analysis, Raya Punuo Fu index using the formula, Prediction Error Method for Parameter Identification- the idea of relaxation, NRnyPWp condition Particle image segmentation and matching subroutines themselves are prepared, Machine learning routines.)

MATLAB 异步电机变转差率控制 simulink 模型 可供参考(Simlink Model of Variable Slip Rate Control for Asynchronous Motor Based on MATLAB)

说明：  Instrument control codes for downloading Matlab waveform file to the signal generator

应用LMS算法对Volterra级数预报，结果收敛，效果很好(Application of LMS algorithm Volterra series prediction, the results of convergence, good results)

Simulink仿真基础《过程控制工程及仿真－(simulink simulation)

随机森林的matlab算法实现，model是训练函数，tree是分类标签函数(Random Forest algorithm matlab, model is the training function, tree classification label function)

Matlab生成Erlang分布随机数的m文件。Erlang分布能拟合其他几乎所有的分布，更具有一般性。(Matlab to generate random numbers m Erlang distribution file. Erlang distribution can fit almost all other distributions, more general.)

在matlab软件中，自己编程实现的一个分类算法，算法首先采用kmeans与fcm聚类分析方法进行采样，然后利用svm对选取得样本进行分类(In matlab software, own programming to achieve a classification algorithm, the algorithm first used fcm kmeans cluster analysis methods and sampling, and then use the elections to obtain samples for svm classification)

Spectral Correlation of Modulated

：在介绍过程监控主要内容及方法的基础上，归纳分析了统计过程监控的发展，详细阐述了传统 的PCA、PCR、PLS、FDA、HMM 和几种改进的PCA方法．指出，将人的经验和专家知识融人到PCA统计过程 监控，提高PCA的故障检测、隔离能力，完善故障诊断与评价能力，将是一个有价值的研究方向(: In the introduction the main content and methods of process monitoring based on the inductive analysis of the statistical process monitoring of development, elaborated on the traditional PCA, PCR, PLS, FDA, HMM, and several improved PCA method. Pointed out that the human experience and financial expertise to the PCA statistical process monitoring of people and improve the PCA fault detection, isolation, and improve fault diagnosis and evaluation capacity, will be a valuable research)