model_kalman

说明：  经典的遗传算法工具箱，谢菲尔德大学的。可以当作雷英杰老师的书学习(GA problem)

Maxwell source code

ESPRINT谱估计，计算DOA，可以与capone进行对比。(ESPRINT spectrum estimation calculates DOA, with capone comparison.)

此源码在matlab环境下运行。此程序功能是：输入采样频率，能够返回高斯滤波器的系数，由连续域经过采样得到离散域的高斯滤波系数。(This source in the matlab environment. This program features are: input sampling frequency, to return the coefficient of Gaussian filter, after sampling from the continuous domain to be discrete domain Gaussian filter coefficients.)

Yen 算法求解最短路径 附加测试代码 例程(Yen shortest path algorithm)

基于滞环控制的单相桥式逆变电路仿真，在2010版matlab仿真(Simulation of single phase bridge inverter circuit based on hysteresis control, matlab simulation in the 2010 Edition)

L-D算法迭代matlab程序，详细流程，易于理解和学习。(LD iterative algorithm matlab procedures, detailed procedures, easy to understand and learn.)

DBF 数字波束直接形成 MATLAB 仿真程序(DBF Digital Beam MATLAB simulation program directly)

说明：  该函数利用高斯频谱产生一维的高斯随机起伏地表。(RSGENG generates 1D Gaussian random rough surfaces with Gaussian Spectrum.)

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.)