lorenz-identification-by-ga

matlab在通信原理中的应用，信道编码循环码仿真，希望大家能用得上！(matlab in Communication Theory in the application of channel coding cycle simulation code, I hope everyone can useful!)

cmos晶体管的matlab仿真和模拟，可以实现cmos晶体管的电压电流传输特性的模拟。(cmos transistors in Matlab simulation and modeling, cmos transistors can achieve the voltage and current characteristics of the transmission simulation.)

模拟混沌与分岔理论，根据沙粒间的相互关系，模拟沙堆崩塌的过程(Analog chaos and bifurcation theory, based on the relationship between sand to simulate the process of sand collapse)

对于学习混沌及设计混沌系统 可以用来参考学习用(For learning the chaotic and design of chaotic systems can be used to refer to learning with)

用遗传算法求取函数的最大值。遗传算法的每一步骤都用子函数的形式给出。适合对遗传算法的入门级选手学习、参考。(Using genetic algorithms to strike a function of the maximum. Each step of the genetic algorithm is given in the form of a subfunction. Suitable for entry-level players genetic algorithm learning, reference.)

用于计算衍射光学元件相位数据，根据给定的输入输出光场振幅分布(Used to calculate phase data of diffractive optics)

无线通信MIMO信道的仿真之系统信道容量(MIMO channel simulation of wireless communication system channel capacity)

位置指令为幅值为1.0的阶跃信号，r（k）=1.0。网络结构为1-4-1，高斯函数的参数值取Cj=[-2 -1 1 2]T ，B=[0.5 0.5 0.5 0.5]T 。 网络权值学习参数为η=0.30，α=0.05 。PID控制各参数的初RBF网络控制，被控对象为G（s）= 取采样时间为1ms，采用Z变换进行离散化，离散化后的被控对象为 y(k)=-den(2)*y（k-1）-den(3)*y(k-2)+num(2)*u(k-1)+num(3)*u(k-2) 始值为，kp=20， kd=0.3， ki=0.1。 (Position command for the step signal amplitude of 1.0, r (k) = 1.0. The network structure is 1-4-1, the Gaussian function parameters taken Cj = [-2-1 1 2] T, B = [0.5 0.5 0.5 0.5] T. Weight learning parameter η = 0.30, α = 0.05. PID control parameters at the beginning of each RBF network control, the controlled object is G (s) = take the sampling time is 1ms, using the Z transform discrete, discretized controlled object is y (k) =-den (2)* y (k-1)-den (3)* y (k-2)+num (2)* u (k-1)+num (3)* u (k-2) initial value, kp = 20, kd = 0.3, ki = 0.1.)

此M文件是当进行批处理时，批量的查询所需的文件(When performing batch processing, batch files needed for the query)

用matlab编写的CART数据挖掘决策树算法(using Matlab CART prepared by the Data Mining Decision Tree Algorithm)