Matlab-jiao-cheng

Gist。 使用直方图的方式提取特征值里面有demo，修改一下可以直接使用，提取特征向量，使用机器学的方法来分类识别( Use the histogram to extract eigenvalues ​ ​ inside demo, change it can be directly used to extract feature vectors, using machine learning approach to classification)

使用sift算法，可以完成两张图片的拼接(stich two image with sift algorithm)

matlab编程代码中的GUI设计非常有用,都是初级设计，希望有说帮助，(some design about matlab gui)

灰色关联度算法，灰色系统分析方法针对不同问题性质有几种不同做法，灰色关联度分析(GreyRelationalAnalysis)是其中的一种(Grey algorithm, gray system analysis methods for different nature of the problem there are several different approaches, gray correlation analysis (GreyRelationalAnalysis) is one of them)

这是中国数学建模中编程交流上分治算法内容，里面提供了很多分治算法的例子(This is the exchange of mathematical modeling in the programming content on the divide and conquer algorithm, which provides many examples of divide and conquer algorithm)

针对混沌参数调制( C P M) 的电力线通信( P L C) 中谐波引起的窄带干扰， 两阶段动态估计方法根据最小 相空间体积( MP S V) 准则估计模型参数， 计算量大。为此， 提出将未知参数合并到增广状态矩阵的联合卡尔曼滤 波方法， 避免了专门的参数估计过程， 在提高增益性能的同时有效降低了计算量。方法的性能通过对混沌电力 线通信下的单音干扰和多音干扰的有效抑制得到了验证。 (For the chaotic parameter modulation (CPM) of the power line communication (PLC) in the narrow-band interference caused by harmonics, two-phase dynamic estimation methods with minimum phase space volume (MP SV) criterion estimate model parameters, calculation of volume. Toward this end, the unknown parameters into augmented state matrix of the joint Kalman filter, to avoid the special parameter estimation process, while improving the performance gain reduces the amount of calculation. Method of performance under the power line communication by chaotic multitone jamming tone interference and the effective suppression has been verified.)

概率论、随机过程、信号系统、信号交织、脉冲成型、扩频序列、幅度调制、频率调制、相位调试介绍及matlb程序仿真(Digital communication principle Based on the matlab simulation )

基于PLAR曲线改变方法实现变声MATLAB程序。(PLAR curve based on changes in methods to achieve change sound MATLAB.)

This program shows equalization of OFDM system.

识别分类算法 matlab程序 PLSDA SIMCA等 机器学习 application 直接运行 带入分类数据 建立模型后运行(Recognition and Classification Algorithms, Programs, Machine Learning Applications, etc. Run directly and bring in classified data to build models.)