WakeUPTimer

一个matlab与stk交互的例子，实现两个软件的互联(an example of interaction between stk an matlab)

svm demo的实现 实现SVM训练测试的全过程，对于初学者很实用，也能彻底了解SVM运行过程，知其然知其所以然，国外一权威网站上下载的，上传以供分享学习(svm demo of SVM training tests to achieve the realization of the entire process, a foreign authority Web site to download and upload for sharing learning)

多目标函数的优化，举例子说明，蚁群算法，不错的学习(good matlab code for ant studying)

说明：  可以计算小孔的圆孔的夫琅禾费衍射图和菲涅尔衍射图样(it can calculate the diffraction of small circular)

ser的比较实现基于mmse_mse和LS_mse (SER compare)

卡尔曼滤波与组合导航原理，非常适合初学者了解原理(Kalman filtering and navigation principles, the principle is very suitable for beginners to understand)

对多元函数动态描述pso算法，可以根据此代码，只要稍微改动，就可以用于其他程序（matlab7.0以上版本）(Dynamic description of multi-function pso algorithm, based on this code, as long as a slight change, it can be used for other procedures (matlab7.0 above))

基于博弈理论的永无WSN节点频谱分配的程序(Based on game theory spectrum allocation procedures)

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.)

语音识别是近年来发展非常迅速的一项计算机智能技术，广泛应用在语音控制、身份识别等多个领域。本次项目主要研究语音识别的预处理过程和特征参数的提取环节。通过对原始语音信号进行预加重和分帧、加窗，滤除低频干扰，提升对语音识别有用的部分，消除了部分噪音和失真。预处理之后进行信号的特征提取，主要选取了短时平均过零率和MFCC两个特征参数，应用matlab软件绘制波形图并提取特征参数矩阵，为之后的语音信号的识别打下了基础。(Speech recognition is a computer intelligent technology which has developed rapidly in recent years. It is widely used in speech control, identity recognition and other fields. This project mainly studies the pretreatment of speech recognition and the extraction of feature parameters. Through pre-emphasis, framing and windowing of the original speech signal, the low frequency interference is filtered out, the useful part of speech recognition is enhanced, and some noise and distortion are eliminated. After pretreatment, feature extraction of signal is carried out. Two feature parameters, short-term average zero-crossing rate and MFCC, are selected. Waveform diagram is drawn by MATLAB software and feature parameter matrix is extracted, which lays a foundation for speech signal recognition.)