Matlab-programming-experience

原对偶内点法求解非线性最优化问题的测试程序，能准确快速的收敛到最优值(Primal dual interior point method for solving nonlinear optimization problem of the test procedure can accurately and quickly converge to the optimal value)

根据csdn版主VC和matlab混编的文章，写成的VC调用matlab生成的动态函数库显示图片。(vc function calls matlab s function )

说明：  基于Harris的掌纹图像特征提取的程序，非常的完整可靠(Harris based on palmprint image feature extraction procedures, very full and reliable)

localization for WSN

含噪声音滤波，去除噪声 含噪声音滤波，去除噪声 含噪声音滤波，去除噪声(Voice Filter Voice Filter Voice Filter)

Matlab阵列信号处理工具箱（DBT），版本2.20，阵列信号处理必备。(Matlab Signal Processing Toolbox Array (DBT), Version 2.20, array signal processing required.)

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)

Now you can apply many of MATLAB’s data analysis functions to this data set.

粒子群算法求解多维约束函数极值，并与遗传算法比较。结果发现，粒子群有很好的精度。(A particle swarm optimization algorithm for solving the extreme value of multi dimensional constrained function, and compared with genetic algorithm. The results show that the particle swarm has a good accuracy.)

matlab/simulink simpowersystems (Three-phase_asynchronous_motor_reversal)