7-5083-0736-4

MIMO系统模型的MATLAB仿真，对于理解MIMO-OFDM系统特点很有好处(MATLAB simulation of MIMO system model for understanding the characteristics of MIMO-OFDM system is very advantageous to)

《matlab揭秘》的各章代码与fig运行结果文件，使用matlab，这本书不错，推荐给大家下载学习。(《Matlab Reveal》 The chapters run the code and fig outcome document, use matlab, this book is good and recommend it to everyone to download learning.)

MATLAB 函数的算法的分形应用以及解析(MATLAB functions of the algorithm and the application of fractal analysis)

对于蜂窝移动通信模型的仿真，有很强的可移植性(Cellular mobile communication model for the simulation, there is a strong portability)

matlab入门到提高，全面讲解了matlab的使用(Introduction to matlab improved overall on the use of the matlab)

some simulation of dtc coommand my name is belmhel ahmed my tel 0797611456

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)

黑箱优化算法，挺不错的，希望大家喜欢(Black-box optimization algorithm, very good, and I hope everyone likes)

输入抽奖号码范围，程序自动产生随机号码，为公平随机长生，前5次为测试号码，第6次为中将号码。(Enter the draw number range, the program automatically generates a random number, random longevity for the fair, the first 5 times for the test number, as will the 6th number.)

Sparsity and incoherence in compressive sampling.pdf