MATLAB-Primer-(7th-Ed)---Timothy-A

说明：  图像混沌加密matlab仿真程序，用MATLAB编程实现的将混沌应用于图像加密的算法.(Chaotic encryption image matlab simulation program, using MATLAB programming will be chaotic image encryption algorithm used.)

美国HL20航天飞机的完整动力学模型以及可视化的实时飞行仿真(U.S. HL20 completed dynamic model of the space shuttle as well as real-time visualization of Flight Simulation)

A new Simpow model DFIG - Doubly-fed Induction Generator

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.)

介绍了RHC控制算法的实现，有具体的例子(Introduced the RHC control algorithm, there are concrete examples)

说明：  标准粒子群优化算法,适用于matlab。方便实用(Standard particle swarm optimization for matlab. Convenient and practical)

基本的直流微电网下垂控制，可以实现在孤岛运行模式下的电压-功率控制(DC Droop control)

Resolution of the problem backpack

MATLAB5.0精要编程及高级应用，机械工业出版社(MATLAB5.0 Essentials programming and advanced applications, Mechanical Industry Press)

时变参数向量自回归模型的估计代码以及模型应用方法(Estimation code and application of Time-Varying parameter vector autoregressive model)