matlabword

数字PID的matlab仿真程序，针对刘金琨的先进PID控制的matlab仿真，里面的部分程序进行了修改 第八章(Digital PID of matlab simulation program for Liu Jinkun matlab advanced PID control simulation, which were modified some procedures of Chapter VIII)

一个效果非常理想的图像颜色变换程序％The goal of this application is to change the colors of a picture, (truecolor or grayscale, *.jpg, *.jpeg), without moving the mouse, looking at them in a slideshow.(One of the effects very good image color conversion process The goal of this application is to change the colors of a picture, (truecolor or grayscale,*. jpg,*. jpeg), without moving the mouse, looking at them in a slideshow.)

handwritten character recognition using back propagation neural network

说明：  现代线性控制理论实验课程的原理，以及有关matlab的例子应用(Modern linear control theory, the principles of experimental program, as well as examples of application of the matlab)

Sample Matrix Inversion is implemented via matlab. the code plot the SINR performance as a function of BER and compare it to the theoretical upper bound BER. YOU CAN CHANGE THE PARAMETERS AND TEST THE RESULTS.

The composition of functions using neural networks. The functions which will be plot: - u(x) - σ(x) - f(x)=σ(u(x))

现有的代数特征的抽取方法绝大多数采用一维的方法，即首先将图像转换为一维向量，再用主分量分析（PCA）,Fisher线性鉴别分析（LDA）,Fisherfaces式核主分量分析（KPCA）等方法抽取特征，然后用适合的分类器分类。针对一维方法维数过高，计算量大，协方差矩阵常常是奇异矩阵等不足，提出了二维的图像特征抽取方法，计算量小，协方差矩阵一般是可逆的，且识别率较高。(existing algebra feature extraction method using a majority of the peacekeepers, First images will be converted into one-dimensional vector, and then principal component analysis (PCA), Fisher Linear Discriminant Analysis (LDA), Fisherfaces audits principal component analysis (KPCA), and other selected characteristics, then use the appropriate classification for classification. Victoria against an excessive dimension method, calculation, covariance matrix is often inadequate singular matrix, a two-dimensional image feature extraction method, a small amount of covariance matrix is usually reversible, and the recognition rate higher.)

Spectral analysis of discrete deterministic signals

Probe into Image Segmentation Based on Sobel Operator and Maximum Entropy Algorithm

Power electronics plays an important role in controlling the grid-connected renewable energy sources. This paper presents a novel adaptive neuro-fuzzy control approach for the renewable interfacing inverter. The main objective is to achieve smooth bidirectional power flow and nonlinear unbalanced load compensation simultaneously, where the conventional proportional-integral controller may fail due to the rapid change in the dynamics of the highly nonlinear system. The combined capability of neuro-fuzzy controller in handling the uncertainties and learning from the processes is proved to be advantageous while controlling the inverter under fluctuating operating conditions. The inverter is actively controlled to compensate the harmonics, reactive power, and the current imbalance of a three-phase four-wire (3P4W) nonlinear load with generated renewable power injection into the grid simultaneously.