Bao-chaotic-system

matlab gui 35个应用实例。压缩包内附有源程序。(matlab gui 35 个 application examples. Archive containing a source.)

说明：  最近在研究数字喷泉码，这是对LT码的仿真，系统XP，语言C++(Recent study of digital fountain codes, LT codes which is the simulation, the system XP, language C++)

在matlab下，利用monte carlo方法模拟光在混浊介质中的传输(In matlab, monte carlo simulation of the transmission of light in turbid media)

matlab 源程序，对于编程的同学很用，大家看看吧(Matlab source for programming with the very students, we look at it)

lear N = 10^5 EB_NO = [-1:30] liu1 = zeros(1,N) liu2 = zeros(1,N) for ii = 1:length(EB_NO) m= (2*(rand(1,N)>0.5)-1) + j*(2*(rand(1,N)>0.5)-1) s = (1/sqrt(2))*m normalization of energy to 1 n = 1/sqrt(2)*[randn(1,N) + j*randn(1,N)]

一个有关于2DPCA算法的源程序，可以准确计算其识别率(A source on the 2DPCA algorithms can accurately calculate the recognition rate)

Run Length Coding Function

This example program demonstrates how to use the on-chip Comparators of the Philips LPC767/768/769 Devices. (This example program demonstrates how to use the on-chip Comparatorsof the Philips LPC767/768/769 Devices.)

主成分分析matlab程序，里面包含子程序，运用时直接调用主程序即可，可用于无监督模式学习(Principal component analysis matlab program, which contains a subroutine, the main program can be called directly when applying, can be used for unsupervised learning mode)

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)