wave

It is a huge matlab document, kinematic represantation and control of Modified Delta Mechanism which consists all S-Function block diagrams.

说明：  简单的遗传算法求最大值的例子，只需按说明修改目标函数即可(Simple example of the maximum genetic algorithms)

UAV zagi parameters for simulation

该MATLAB代码可以实现一般的YUV攻击测试。(The MATLAB code can achieve a general YUV attack test.)

曲柄滑块机构的运动学仿真，研究曲轴运动的物理规律，通过建模和仿真，其运动规律可以实时动画展现，各项数据实时输出，大大增加了直观性(Slider-crank mechanism kinematics simulation, the laws of physics research crankshaft movement, through modeling and simulation, which show the movement of the animation in real-time, real-time output of the data, greatly increasing the intuitiveness)

三电平三相桥式逆变器的SVPWM控制方式仿真（应用S函数）(Three-level three-phase bridge inverter SVPWM control simulation (Application S function))

基于matlab的并网逆变仿真器，可以动态仿真(Matlab-based grid inverter emulator can dynamic simulation)

matlab code code for binary modulation in fsk

基于模糊控制的PID温度控制器的设计 :将模糊控制和PID 控制相结合起来，通过模糊控制实现PID 参数自适应的方法来控制系统温度。这种Fuzzy- PID 策略，模糊控制的采用不是代替PID 控制，而是对传统控制方式的改进和扩展，它既保持了常规PID 控制系统结构简单、使用方便、鲁棒性强、控制精度高的优点，又采用模糊推理的方法实现了PID 参数PK、IK、DK的在线自整定，兼具了模糊控制灵活性、适应性强的特点，相比单纯的任一种控制效果都要好(PID temperature controller design based on fuzzy control: fuzzy control and PID control combine to achieve PID adaptive approach to control system temperature by means of fuzzy control. This Fuzzy- PID strategy is not to replace the use of fuzzy control PID control, but the traditional control methods to improve and expand, both to maintain a simple and conventional PID control system structure, easy to use, robust, and high control accuracy advantages, but also the use of fuzzy inference PID parameters to achieve the PK, IK, DK-line self-tuning, both the fuzzy control flexibility, adaptability features, compared to simply have a good control effect either)

1. 按照式计算相似性矩阵 2. 排除自身的相似度 3. 按照式计算归一化矩阵 4. 按照式计算归一化拉普拉斯图矩阵L 5. 计算L的特征向量，将前个特征值最大的向量按列放置成一个矩阵，即依次为前个特征值最大的特征向量 6. 归一化X形成矩阵Y 7. 对矩阵Y按每行为一个数据点，进行k-means聚类，第行所属的类就是原来所属的类。(1. similarity matrix based on formula computation 2. rule out your own similarity. 3. normalized matrix based on formula computation 4. normalized formula Laplasse matrix L according to formula. 5. Calculate the eigenvectors of L. Place the vectors with the largest eigenvalues in columns into a matrix, that is, the eigenvectors with the largest eigenvalues in turn. 6. normalized X formation matrix Y 7. K-means clustering is applied to matrix Y according to one data point per behavior. The first row belongs to the original class.)