p-control-of-PID

BPSK仿真，Eb/N0=0:10,得出BER与Eb/N0的关系曲线，AWGN信道(BPSK simulation, Eb/N0 = 0:10, draw BER versus Eb/N0 of the curve, AWGN channel)

算法程序实现了多层次灰色综合评价，并且有层次分析法的对比，三层的。(Algorithm program to achieve a multi-level gray comprehensive uation, and contrasting the Analytic Hierarchy Process, three layers.)

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.)

Simple Genetic Algorithm coded in matlab

finite-time稳定控制算法，李雅普诺夫方法得出LMI算法，进而求出反馈增益矩阵(finite-time stability control algorithm)

说明：  异步电机适量控制的仿真，并用卡尔曼滤波做了无速度传感器(Simulation of the appropriate amount of asynchronous motor control, and using Kalman filter to make a speed sensor)

用于MATLAB 6.0 (R12)环境 Dijkstra最短路由算法源代码 ( MATLAB 6.0 (R12) Dijkstra Shortest Path Routing )

the above function is useful to find the derivative of a bspline

noise removal using spatial filter

clustering data file