diodeetC

共有5个程序，分别是从文件提取散点画球（椭球），散点画压力云图，散点画散点位置及速度分布图，散点画密度分布图，以及从数据文件中提取特定散点的程序，这是一套程序，可对大量散点数据进行处理。(There are five programs, which are extracted from the file powder stippling ball (ellipsoid), scattered cloud pressure stipple, stipple scatter scattered distribution of position and velocity, bulk density stipple maps and data files extracted from the scatter-specific procedures This is a process, a large number of discrete data can be processed.)

SMI方向图和性能(波束形成算法，自己修改） 优点： 收敛速度快 缺点：1 当阵元输出含有较强的期望信时，或者期望信号与干扰信号相关时，性能急剧下降.2 由于权向量含有方向矢量，因此对阵列的幅相差非常敏感；3 ,期望信号的功率不能过大，比干扰信号相差几十dB，也就是说，在小期望信号、大干扰信号情况下，也可进行SMI处理。 (SMI pattern and performance (beamforming algorithm, make changes to) Advantages: fast convergence Disadvantages: 1 output when the array elements with strong expectations of the letter, or related to the desired signal and interference signals, the performance sharply .2 direction vector containing the weight vector, so the array is very sensitive to the amplitude difference 3, the desired signal The power can not be too large, than the difference between the scores of interference signal dB, that is, in the small desired signal, large signal interference case, SMI can also be handled. )

《MATLAB神经网络30个案例分析》中的第28个例子，案例28 灰色神经网络的预测算法—订单需求预测。希望对大家有一定的帮助！(The MATLAB neural network analysis of 30 cases of 28 example, 28 cases of grey neural network prediction algorithm- order demand forecasting. Hope to have certain help to everybody! )

Mechanism Design Slider crank Mechanism

标准的粒子滤波器matlab程序，附有详细注释，可以帮助有效掌握粒子滤波算法。希望大家多提建议：）(standard particle filter Matlab procedures, with detailed notes, can help grasp the particle filter. We hope more suggestions :))

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that assigns new examples into one category or the other, making it a non-probabilistic binary linear classifier. An SVM model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall on.

goss load flow for pq bus and good for every system

matlab源码，解决非线性最小二乘问题的优化算法源码，可修改(Matlab source code, to solve the nonlinear least squares optimization algorithm source code, can be modified)

ebook about system design and modeling with simulink

实例，简单的介绍了matlab的基本知识，适合初学者，希望对大家有帮助(Instance, a simple introduction to the basics of matlab, suitable for beginners, we want to help)