libsvm-mat-2.91-1

slm ccdf codes in matlab

skype dotnet application sample

256QAM调制及解调程序，采用格雷码编码格式。(256QAM modulation and demodulation)

对模糊控制器的设计以及matlab模糊工具箱的学习比较有帮助(pair of fuzzy controller design and fuzzy Matlab toolbox learning more help)

在FFT后信号的频率往往与谱线不相重合，而在两条谱线之间。为了要求得信号的频率、幅值和初始相角，必须要使用修正法来完成。这里给出一种修正法。(After the signal in the FFT frequency spectrum often do not coincide, and in between the two lines. Request for a signal frequency, amplitude and initial phase angle, we must use to complete the Amendment Act. Here are a Amendment Act.)

本文件包含一个word文档，详细讲解互信息的计算，以及一个txt文档，包含MI的matlab源代码。(This document contains a word document, explain in detail the calculation of mutual information, as well as a txt file, including MI, Matlab source code.)

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.)

说明：  现代信号处理教程（胡广书）的 WORD电子版一本电子信息专业研究生教程。很好的教材(Modern signal processing tutorial (Guang-Shu Hu) in WORD electronic version of an electronic information postgraduate tutorial. Good teaching)

假设一个接收到的信号为：x(t)=s(t)+n(t), 其中s(t)=A*cos(wt+a), 已知信号的频率w=1KHz， 而信号的幅度和相位未知，n(t)是一个服从N(0,1)分布的白噪声。为了利用计算机对信号进行处理， 将信号按10KHz的频率进行采样。 通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t). 在未知信号频率的情况下，通过对x(t)进行LMS自适应信号处理，从接收信号中滤出有用信号s(t). (Assuming a received signal: x (t) = s (t)+ n (t), one of s (t) = A* cos (wt+ a), the known frequency signal w = 1KHz, And the signal amplitude and phase of the unknown, n (t) is a subject to N (0,1) the distribution of white noise. The use of computers in order to deal with the signal, The signal by sampling frequency of 10KHz. Of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t). Frequency signals in unknown circumstances, the adoption of x (t) for LMS adaptive signal processing, filtering from the received signal in the useful signal s (t).)

Vsfm to Matlab by Bashar Alsadik 05 Feb 2014 Vsfm to Matlab