zhongzhilvbo

说明：  beamlet变化的工具箱，利用beamlet的相关知识。(Toolbox beamlet change, using knowledge beamlet.)

运用Matlab对晶闸管整流逆变进行建模仿真，可以改变导通角，负荷等参数对该电路的性质和相关参数设置进行分析研究(To use Matlab modeling and simulation, thyristor rectifier inverter can change the conduction angle, load parameter settings of the nature and parameters of the circuit analysis)

基本的boost PFC simulink仿真，带有功率因数测量。控制采用滞环控制，功率因数可达99 以上。(The basic boost PFC simulink simulation, with a power factor measurement. Control using hysteresis control, power factor up to 99 .)

声纳技术实验常用水声信号及其处理方法，包括CW信号产生，LMF信号产生(Sonar technology experiments commonly used in underwater acoustic signal processing methods, including the CW signal generator, Model LMF signal generator)

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.)

本程序可进行植被物候研究，用于提取植被返青期、枯黄期及生长季长度等物候参数。(This procedure can be carried out vegetation phenology studies, used to extract reviving vegetation, yellow and growing season length of phenological parameters.)

alamouti code with 2 receiver and 2 transmiter,with matlab code in advanced comminucation,

电网络matlab编程，对复杂电路用程序进行编程计算。(Electricity network matlab programming and application programming calculation complex circuit)

设计一个程序实现基于Minimum Degree算法的稀疏矩阵Reordering算法。(Design a program based on sparse matrix algorithms Minimum Degree Reordering algorithms.)

particle swarm optimization for loss reduction