SCARA

完成输入序列的msk调制功能，输入序列必须是0 1(Complete input sequence of msk modulation, the input sequence must be 01)

在matlab平台下产生信号序列通过QPSK方式调制通过高斯信道以及瑞利信道，对其中的过程的信号时域以及其频谱，星座图情况进行分析(Matlab platform in the signal sequence under way through the QPSK modulation, as well as through the Gaussian channel Rayleigh Road, on which the process of time-domain signal and its spectrum, constellation diagram analysis)

基于Matlab的识别程序： 应用于指纹分析及识别系统(Matlab-based identification procedures: application to fingerprint analysis and identification system)

本程序是基于bp神经网络的分类程序 开发环境是matlab(matlab)

针对实际工业生产中常见的多输入多输出时滞过程, 构造多变量Smith预估控制结构, 提出基于对象模 型伴随矩阵的解耦器设计方法. 通过对解耦后对象的幅频和相频特性分析, 获得对象的简化一阶数学模型. 根 据Smith预估控制结构闭环特征方程的特点, 利用Butterworth滤波器极点配置的原理, 对解耦后的多变量时滞过程 设计PI控制器. 结合实际过程中常见的不确定性, 分析了控制系统保证鲁棒稳定性的充要条件. 最后以实例验证了 本文所提方法的优越性.(The decoupling Smith control method is presented for multi-input-multi-output system with time-delays which are often encountered in practical engineering. A new method for designing the decoupler based on the adjoint matrix of the multivariable system model with time-delays is proposed. By analyzing the amplitude-frequency and phasefrequency characteristics of the decoupled model, the decoupled model is reduced to a first-order model with time-delay. According to the closed-loop characteristic equation of Smith predictor structure, a PI controller is developed by using the principle of pole assignment for Butterworth filter. Sufficient and necessary conditions for robust stability are derived for adaptive and multiplicative uncertainties commonly occurred in practice. Simulation results show the superiority of the proposed method.)

光伏阵列Simulink仿真文件 光伏阵列(PV array Simulink simulation files)

基于matlab的模糊pid的仿真研究，有详细的解说如何等价被控对象的传递函数，如何模糊等等。(fuzzy pid)

说明：  %solve u_xx + u_yy = f(x,y) % over the region D = [x0,xf,y0,yf] = {(x,y) |x0

HIGH POWER AMPLIFIER MODELING

This tutorial is designed to give the reader an understanding of Principal Components Analysis (PCA). PCA is a useful statistical technique that has found application in fields such as face recognition and image compression, and is a common technique for finding patterns in data of high dimension. Before getting to a description of PCA, this tutorial first introduces mathematical concepts that will be used in PCA. It covers standard deviation, covariance, eigenvectors and eigenvalues. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.