lipsize

rate equation with simulink of matlab

一个关于matlab优化工具箱GOT中的遗传算法函数（GA）和全局优化算法函数（Globalsearch）优化能力的小对比，前者寻优快，但是结果不稳定，在风电场协同有功出力优化上面甚至可能比不过传统的单个风机层面的优化。后者计算量大，耗时较长，但是每次计算结果稳定，较传统单台风机层面优化有少量的提升。Tips:1,本文风场的建模只是采用了PARK模型，实际风场的气动过程应该还要复杂一些，未经实际风场验证，权当是工程优化入门 2,单个风机层面的功率系数Cp参数来源于NREL 5MW模型，跟实际叶片气动性能有关，可靠性未知。3,这种风场级别的协同优化主要考虑尾流因而更加适用于地形较平坦的风电场。《基于风机运行参数协同设定的风电场有功出力优化》是部分由这个程序支撑的，欢迎交流。(The classical operation strategy of a wind farm works with a principle that each wind turbine converting as much aerodynamic power as available the incoming airflow. But this does not guarantee that the power converted by the whole wind farm be a maximum due to the wake effect. Unlike the conventional operation, this paper proposes the collaborative setting of the operation point of each turbine so that the overall production of the wind farm is maximized. The optimization is performed by Matlab Global Optimization Toolbox(GOT) based on a PARK wake model accompany with the area-weighted quadratic sum method. The strategy was proved applicable in many cases by sensitivity analysis. As an important side effect, the proposed method also allows decreasing the added turbulence, hence the mechanical load produced by wakes. As a consequence, the overall wind farm availability is increased. )

个多目标跟踪的matlab代码，里面附有说明文档。其中背景差分法可以实现实时更新。该代码在检测与跟踪少量目标时效果很好，但是跟踪目标大于8后，效果下降很多(matlab)

上面都是最新的LDPC编码技术，希望对大家都有用哈，我觉得十分的有用(The above are the latest LDPC coding techniques, I hope everyone Ha, I think is very useful)

graphic user interface m files of matlab

Simple surface recostruction program based on Crust algorithm Given a set of 3D points returns a triangulated tight surface.

用matalb进行ofdm的仿真，这个程序可以运行，简单明了(matalb used for the ofdm simulation, the process can run simple and clear)

induction motor simulation for performing modern control and other controllers

这是一个展示Binding功能的例子，实现了滑动条控制矩形坐标的功能。(This is a showcase Binding features examples slider control to achieve a rectangular coordinate functions.)

说明：  最速下降法是迭代法的一种,可以用于求解最小二乘问题(线性和非线性都可以)。在求解机器学习算法的模型参数，即无约束优化问题时，梯度下降（Gradient Descent）是最常采用的方法之一，另一种常用的方法是最小二乘法。在求解损失函数的最小值时，可以通过梯度下降法来一步步的迭代求解，得到最小化的损失函数和模型参数值。反过来，如果我们需要求解损失函数的最大值，这时就需要用梯度上升法来迭代了。在机器学习中，基于基本的梯度下降法发展了两种梯度下降方法，分别为随机梯度下降法和批量梯度下降法。(The steepest descent method is a kind of iterative method, which can be used to solve the least squares problem (both linear and nonlinear). In solving the model parameters of machine learning algorithm, that is, unconstrained optimization, gradient descent is one of the most commonly used methods, and the other is the least square method. When solving the minimum value of loss function, the gradient descent method can be used step by step to get the minimum value of loss function and model parameters. Conversely, if we need to solve the maximum value of the loss function, then we need to use the gradient rise method to iterate. In machine learning, two kinds of ladders are developed based on the basic gradient descent method)