MATLAB-analysis-cantilever-problem

有偏卡尔曼滤波器实现，可以用来消除无线定位中的非视距误差(Biased Kalman Filters can be used to eliminate wireless location in NLOS error)

基于台湾大学林智仁教授开发的LSSVM工具箱编写的鸢尾属植物分类程序，基于四个特征中的后两个进行的分类，分类效果相当好！(classification for iris data using lssvm)

经典功率谱估计及其仿真,可以给有需要的同志参考，欢迎下载(Classic power spectrum estimation and simulation)

针对Windows平台的Subversion客户端Version1.8，详细介绍Tortoise客户端，对软件编程人员有很大帮助。(For Windows platform Subversion client Version1.8, detailing Tortoise client software programmers are very helpful.)

说明：  结构动力学算例，运用mirison和matlab语言，写了一个波浪力的算例(A numerical example of structural dynamics is given. a numerical example of wave force is written by using mirison and MATLAB language.)

CODE FOR COLOR IMAGE COMPRESSION USING QUANTIZATION ALORITHM. VECTOR QUANTIZATION.

如何生成可独立运行的Matlab程序.介绍了Matlab C数学库、Matlab C++数学库、Matcom、Matlab COM Builder、Matlab Engine及编译Matlab独立可执行程序等Matlab混合程序设计的内容。 (How to build can be run independently of Matlab programs)

说明：  matlab编写的进行地震处理中的速度分析，包括速度转换等内容(matlab write speed of processing seismic analysis, including speed of conversion, etc.)

利用朴实贝叶斯方法求解有关肺炎的问题。肺炎对应有四个特征：发烧、疼痛、咳嗽和血细胞异常，当确定了患肺炎与否时，四个特征条件独立。假设患肺炎与否和四个特征都可表示为Ture和False。 根据pneumonia.tex文件中的数据（500行，每行前4个数对应4个特征变量，第5个数对应患肺炎是否为真，以0表示False，1表示Ture），编写Matlab 程序 maininference.m，从example.txt中读取病人的症状信息（0表示False，1表示True，-1表示not given），并计算对应的患肺炎的可能性。所给的信息按发烧、疼痛、咳嗽、血细胞异常的顺序排列。将计算结果保存在answer.txt中。(Simple Bayesian method to solve the question of pneumonia. Pneumonia corresponding four characteristics: fever, pain, cough and blood cell abnormalities, determine the risk of pneumonia or not, the four characteristics of conditional independence. Assumptions suffering from pneumonia or not and four features can be represented as Ture and False. According to the data pneumonia.tex file (500 lines, each line of the first four numbers corresponding to the four characteristic variables, number 5 corresponds to the risk of pneumonia is true, 0 represents False, said Ture), to write Matlab program maininference. m from example.txt read the information of the patient' s symptoms (0 = False, said True, the-1 indicates not given), and to calculate corresponding to the likelihood of suffering from pneumonia. The information given by the order of fever, pain, cough, blood cell abnormalities. The calculated results is be saved in answer.txt.)

this folder contains matlab codes for minimize sphere with pso algorithm.