langevinphasefield

GMM PARAMETERS AN BE EASILY IMPLIMENTEED USING EM TECHNIQUE.EVENTHOUGH THIS MODEL IS COMPLEX ,E XPECTATION MAXIMAISATION FINDS AN EASYWAY TO CALCULATE THE PARAMETERS IN AN EFFICIENT WAY.

计算图像的平均梯度和边缘强度的matlab源码，M文件形式(Calculating the average image intensity gradient and the edge of the matlab source, M documents in the form)

Practical introduction to Matlab      

DTW is one of the method to identify speech and signature or any biometric hope to enjoy it

simple embedded web server

实现典型相关分析，电力系统暂态稳定程序，可以进行暂态稳定计算,IMC-PID是利用内模控制原理来对PID参数进行计算。( Achieve canonical correlation analysis, Power System Transient Stability Program, can be transient stability, The IMC- PID is using the internal model control principle for PID parameters is calculated.)

说明：  正弦波方波通过低通滤波器，显示时域频域波形，自相关函数，功率谱密度(The sine wave and square wave pass through the low-pass filter to display the time-domain waveform, frequency domain waveform, autocorrelation function and power spectral density)

本程序实现1D声波正演，擦用Clyton单程波吸收边界条件。(This program implements 1D acoustic wave equation, rub with Clayton one-way wave absorbing boundary condition)

基于多相结构的信道化接收机，合成孔径雷达（SAR)目标成像仿真，cordic算法的matlab仿真。( Channelized receiver based on multi-phase structure, Synthetic Aperture Radar (SAR) imaging simulation target, cordic matlab simulation algorithm.)

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)