YUV2RGB

全面讲解matlab7.0的原理与使用方法(Fully explain the principle and use matlab7.0)

change a class for a bi-class s database

三相异步电机的矢量控制的模型，希望对大家有所帮助(Vector control，hope that)

利用HFSS-MATLAB-API编写的程序，该程序可以生成一个同轴馈电的贴片天线的vbs脚本程序，并和hfss软件自动连接，算出该天线的S11参数。(HFSS-MATLAB-API programs written in the program can generate a coaxial fed patch antenna vbs script to automatically connect and hfss software, calculated S11 parameter of the antenna.)

Kode Program Algoritma Nearest Neighbor

里面是小波分析的实现方法，还有数学介绍。数字信号处理上机实验答案(第三版).docx的电子版(There is a wavelet analysis method, as well as mathematical description. Digital signal processing on the experimental answer (third edition). Electronic version of docx)

这是非线性薛定谔方程的源代码m文件，可以帮助研究(This is the source code of Nonlinear Schrodinger equation)

说明：  讲解MATLAB在数学各分支中的应用 函数和命令 应用与技巧(MATLAB in mathematics to explain the various branches of functions and commands in the application of Tips and Tricks)

matlab实现软硬阈值函数的图像去噪，附带一张测试图片矩阵lenna.mat(matlab image denoising hard and soft threshold function, with a test image matrix lenna.mat)

本文采用小波神经网络进行交通流量预测，短时交通流量存在随机性和非线性因素，影响预测的准确性。传统预测模型难以反映交通流量变化特点，同时传统神经网络易陷入局部极小值，泛化能力差，交通流量预测精度低。为了提高短时交通流量预测精度，提出一种小波神经网络的短时交通流量预测模型。小波神经网络可以对短时交通流量随机性、不确定性进行局部分析，并进行非线性预测，验证了模型的有效性，进行了对比试验。验证结果表明，小波神经网络提高了短时交通流量预精度，预测结果更具应用价值。(In this paper, wavelet neural network is used to forecast traffic flow. There are random and nonlinear factors in short-term traffic flow, affecting the accuracy of prediction. The traditional prediction model is difficult to reflect the characteristics of traffic flow changes. At the same time, the traditional neural network is easy to fall into the local minimum, the generalization ability is poor, and the prediction accuracy of traffic flow is low. In order to improve the accuracy of short-term traffic flow forecasting, a short-term traffic flow prediction model based on wavelet neural network is proposed. The wavelet neural network can analyze the randomness and uncertainty of short-term traffic flow and perform nonlinear prediction. The validity of the model is verified and a comparative experiment is conducted. The verification results show that the wavelet neural network improves the short-term traffic flow pre-precision, and the prediction result has more application value.)