Proj_Findlow

改进的卡尔曼滤波程序，并比较了传统卡尔曼滤波的性能(UKF)

中文文本分词系统+基于贝叶斯算法的文本分类源码，用matlab实现。(Chinese word segmentation system+ based on Bayes text classification source code, using matlab implementation.)

说明：  按FPE定阶的 源程序：fpe.cpp M序列：M序列.txt 白噪声：Gauss.txt 程序中先用依模型阶次递推算法估计模型的参数，再用fpe方法判断模型的阶次。 程序运行结果如下： n: 1 判断阶次FPE的值: 0.0096406 -0.481665 1.07868 n: 2 判断阶次FPE的值: 0.00875755 -0.446739 0.00498181 1.07791 0.0527289 n: 3 判断阶次FPE的值: 0.0087098 -0.459433 0.120972 -0.0569228 1.07814 0.0390757 0.116982 n: 4 判断阶次FPE的值: 0.000396884 -0.509677 0.4501 -0.200906 0.0656188 1.07991 -0.0156362 0.442989 0.0497236 n: 5 判断阶次FPE的值: 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771 n: 6 判断阶次FPE的值: 3.23349e-007 -1.14659 0.76933 -0.487651 0.329676 -0.10377 -0.00440907 1.07999 -0.703574 0.447253 -0.235282 0.113587 0.00479688 从以上结果可以看出，当n=5时，fpe值最小，所以这时的模型阶次和参数估计值为最优结果： 3.2095e-007 -1.18415 0.813123 -0.517862 0.34881 -0.116864 1.07999 -0.744141 0.474462 -0.253112 0.122771(err)

包括MATLAB的代码:MIMLSVM+和E-MIMLSVM+(MIMLdros includes the MATLAB codes of MIMLSVM+ and E-MIMLSVM+ which are MIML algorithms that have been applied to drosophila gene expression pattern annotation. The package contains a Readme file which explains how to use it.)

Describe: You can simulate a simple process MSVM Face recognition in achieving the entry code on them

kalman filter code for position detection

电力系统暂态稳定仿真模型，设置了短路类型，绘制转速和功角曲线，可分析发电机转速和功角特性，可以设置不同的系统参数和短路类型。(Power system transient stability simulation model, set a short-circuit type, draw speed and power angle, generator speed and power angle characteristics, you can set different system parameters and fault types.)

声纹识别的特征参数提取,盲信号用于语音识别maltab程序代码,里面有相关语音文件，很不错 (Voiceprint identification of the feature extraction and blind signal for speech recognition maltab code, which has the relevant sound files, very good)

Area Spectral Efficeinecy

Matlab将figure系列另存为Gif动画(Save series of figures as gif with Matlab)