tomography-modelling

图像识别概述统计模式的识别方法、结构语句的识别方法、模糊集识别法、神经网络识别法等(Image recognition methods outlined in the statistical model identification, structure identification method statements, fuzzy set identification method, neural network identification method, etc.)

普通的PID控制，程序正确，下载就能用，只需要改PID控制的三个参数即可(Normal PID control, the program correctly, you can use to download, just change the parameters of PID control can be of three)

matlab7.0应用教程，涵盖了mantlab的所有基础知识，是一本想学习matlab的一本不可多得的教材(matlab7.0 application tutorial, covering all the basics mantlab is a want to learn matlab' s a rare textbook)

自行车的非线性控制理论和建模, 英文的文件， 不过很全面 值得一看(non linear modelling and test of bike)

说明：  这是一个建立在OMP算法基础上，笔者自己提出来的一个稀疏分解的DOA估计算法，利用阵列流形矢量间的不相关进行匹配追踪选择，选出与信源位置流形最接近的，以便求得DOA(This is a building based on the algorithm of OMP, the author himself a sparse decomposition algorithm for DOA estimation, using the array manifold a vector between the related matching track selection, and choose the source position manifold closest, in order to obtain DOA)

阵元间隔为半波长的均匀分布16元线阵，预成指向士arcsin（(2i-1)/16）(i=l，2，…，8)的16个均匀加权常规波束,远场有5个互不相关的目标源发射信号，每个源相对于基阵的方位为-40，-42，-5，30和33，且每个信号源到达基阵的信噪比相同，均为5DB，快拍数为1000.(Array element spacing of half wavelength for the uniform distribution of 16 per linear array, pre-point disabilities into arcsin ((2i-1)/16) (i = l, 2, ..., 8) of 16 evenly weighted conventional beam, far-field has 5 unrelated target the source of emission signals, each source array relative to the position for the-40,-42,-5,30 and 33, and each signal source to the array signal to noise ratio the same as Scientology, are 5DB, snapshots for 1000)

说明：  《数字信号处理教程——MATLAB释义与实现》的例程。对学习matlab数字信号处理有帮助。(Digital Signal Processing Tutorial- MATLAB interpretation and implementation)

自适应滤波matlab源程序，含有波形仿真程序，以及自适应滤波原理，lms算法介绍。(Adaptive filter matlab source code, containing the waveform simulation program, as well as the principle of adaptive filtering lms algorithm is introduced.)

说明：  数学建模学习资料 并行算法，用以数学建模学习(Parallel algorithm for learning data of mathematical modeling)

机器学习中的垃圾邮件分类程序，用matlab做的。从以下链接下载垃圾邮件数据（spam data）：(数据已下载，放在spambase.zip) http://www-stat.stanford.edu/~tibs/ElemStatLearn/index.html 该数据包含57个邮件信息相关的变量，每条邮件可以被分类为垃圾邮件（Y=1）和非垃圾邮件（Y=0）。输出Y的值在文件中每一列的末尾。练习的目标是要预测电子邮件是否为垃圾邮件。 (Machine Learning spam classification procedures, using matlab to do. Data (spam data) from the following link to download the junk mail: (data has been downloaded, put spambase.zip) http://www-stat.stanford.edu/ ~ tibs/ElemStatLearn/index.html The data includes 57 e-mail messages related variables, each message can be classified as spam (Y = 1) and non-spam (Y = 0). Y value of the output end of each column in the file. The goal is to predict exercise email is spam.)