fengchang

温度控制的simulink仿真程序，可以运行(simulink)

IEEE14节点进行牛顿拉弗逊法计算电力系统潮流计算(IEEE14 node calculates Newtonian Raphson method for power flow calculation)

in this project we develop a source code in matlab which take attendance automatically, The source code and data base are available in this file

This is for testing a matlab program.

edge detection sobel

本实验语音库为免费的柏林情感语音库，其采样频率为16KHZ，16bit量化。该语音库共有500 句情感语音信号，分别由十名专业演员(5 男，5 女)在不同情感状态下（高兴、愤怒、平静、悲伤、害怕、厌烦、憎恨）朗读十句不同文本的德语组成。本实验选取其中的部分情感（高兴、愤怒、悲伤）加以识别。仿真实验环境为MATLAB7.0。 实验选取的情感特征为短时平均能量、短时平均幅度、基频和短时过零率。为了降低不同人在表达不同情感时的个人差异造成的影响，本文实验过程中将提取的情感特征进行归一化处理。归一化采取将同一个人的三种情感语音信号的情感特征放在一起归一化处理，并将归一化后的情感特征作为KNN分类器的训练样本和测试样本。 该实验使用了传统的KNN算法和改进算法两种识别方式，通过实验结果对比了各自的特点。 (This experiment YuYinKu for free Berlin YuYinKu emotion, the sampling frequency for 16 KHZ, 16 bit quantification. The YuYinKu 500 words of emotional speech signal, respectively by ten professional actors (5 male, five female) in different emotional state (happy, anger, sadness, fear, calm, bored, hate) read ten sentences in different text of the German. This experiment selection of part of the emotions (joy, anger, sadness) is recognized. The simulation experiment environment for MATLAB7.0. The selection of emotional characteristics for short-term average energy, short-term average amplitude, frequency, and the short zero rate. In order to reduce the different people in different emotional expression of the impact of individual differences, the experiment process will extract emotional characteristics is normalized. The normalized will take the same three kind of emotional speech signal emotional features normalized put together, and after the normalized the emotional characteristics)

克里金近似模型是一种很好的近似模型，使用非常广泛。(Kriging approximation model is a good approximation model is widely used.)

variable step-size fourth-order Adams Predictor-Corrector method

matlab源码，用于实现基于内容的图像检索(matlab source for content-based image retrieval)

最小二乘是广义预测的基础知识，学习最小二乘有利于学习广义预测控制(Generalized least squares is predicted on the basis of knowledge, learning conducive to learning generalized least squares predictive control)