liziqun

minimum-volume enclosing simplex

Struss code Matlab code

面板数据模型的matlab程序（以香烟为例）(Panel data model matlab program (to cigarettes as an example))

一个关于温度场的Matlab的源程序可以查询不同坐标的温度(A temperature field on Matlab source temperature can query different coordinate)

有关蚁群算法的matlab程序，希望对大家有所帮助，有问题共同讨论，(On the ant colony algorithm matlab procedures, everyone would like to help, have problems to discuss,)

基于matlab编写的源码 做简单的数字识别(Digital Identification Digital Identification Digital Identification Digital Identification Digital Identification Digital Identification Digital Identification Digital Identification Digital Identification Digital Identification)

说明：  高斯混合概率假设密度滤波器，适合于非线性多目标跟踪。(Gaussian Mixture Probability Hypothesis Density Filter,It is suitable for nonlinear multi-target tracking.)

it has different sources

迭代法求解线性方程组，首先将方程组AX=B中未知数X给定，计算出矩阵B。然后把矩阵B带入，利用雅各比迭代法反求X(Iterative method for solving linear equations, the first of equations AX = B will be in the unknown X given to calculate the matrix B. Then the matrix B into the use of Jacobi iterative method of reverse X)

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.)