SimuClutter

music algorithm for variation of snr

ls-svm理论算法详解,原作者论文原著(ls-svm Detailed theoretical algorithm, the original author of the paper)

MATLAB仿真技术与实例应用教程，其中有很多仿真实验，对于初学者尽快熟悉matlab的使用是很有用的！(MATLAB simulation technology and examples of application tutorials, of which there are many simulation matlab use is useful for beginners to familiarize themselves!)

kalman源程序。用MATLAB编制。主要用于统计预测，也可用于其他行业。(kalman program for prediction.)

基于HS边界润滑条件和雷诺方程流体润滑模型的求解程序(Solution procedure of fluid lubrication model based on HS boundary lubrication condition and Reynolds equation)

raytracing for optical waveguides.

another optimization project solve by amin zafarian

基于matlab平台有理分式多项式法模态参数识别(Based on matlab platform rational fraction polynomial method modal parameter identification)

MATLAB编的贪吃蛇，可以算则难度，地图，有音乐。一个不错的小程序。(MATLAB code of the Snake, you can count the degree of difficulty, maps, musical. A nice little program.)

多变量输入、输出、多干扰、非线性和强耦合的复杂系统控制是一个比较困难的问题，常用的控制器可能因为多变量耦合问题难以控制系统。PID神经元网络是一种多层前向神经元网络，它的各层神经元个数、连接方式、连接权值是按照PID控制规律的已有原则和经验确定的，是一种动态的符合控制系统的前向网络。但是由于PID网络初始权值随机取值的原因，每次控制的效果都有所差别，个别情况下控制效果还比较差。本案例研究了基于PID神经元的多变量耦合系统控制，并用PSO算法来优化控制器以取得更好的控制效果。(Multivariable input, output, and more interference, complex nonlinear and strong coupling system control is a more difficult problem, commonly used multivariable controller may be because the problem is difficult to control the coupling system. PID neural network is a multi-front to neural networks, its number of neurons in each layer, connection, connection weights are in accordance with the existing principles and experience to determine the PID control law is a dynamic compliance before the control system to the network. However, due to the random initial weights of the network PID values ​ ​ of reason, every time there are differences in the effect of control and in some cases the control effect is still relatively poor. This case study of multivariable coupling system based on PID control neurons, and with PSO algorithm to optimize the controller to achieve better control effect.)