Kmeans

运动人体行为分析的资料，利用因马尔科夫模型和支持向量基做的(Human Movement behavior analysis data by using support vector-based model and HMM)

数字控制系统仿真与综合应用：设控制对象为 W1=10/s(1+0.1s)(1+0.05s),采样周期T=0.2s。 （1）采用零阶保持器，将W1(s)转换成W1(z),串接一个计算机调节模型D(z)组成一个典型的数字反馈系统。分别用仿真方法得到系统在单位阶跃输入作用下的响应和系统在单位速度输入时的输出响应。 (Digital control system simulation and comprehensive application: set control object for W1 = 10/s (1+ 0.1 s) (1+ 0.05 s), the sampling period T = 0.2 s. (1) the zero order keep device, will W1 (s) convert W1 (z), the combination of a computer adjusting model D (z) composed of a typical digital feedback system. The simulation method were used to get in the unit step input system under the action of response and system in the unit of output response speed input. )

用matlab实现神经网络算法用于锅炉故障检测，结果可实现(Using matlab neural network algorithm for boiler fault detection, the results can be achieved)

根据系统辨识的系统模型，采用KALMAN滤波算法，根据期望信号和观测信号求未知的系统。(According to the system model system identification, using KALMAN filtering algorithm, based on the desired signal and the observed signal seeking unknown system.)

阵列信号处理_空间谱估计原理的一些基本算法——MusicEspritMp(Array signal processing _ the principle of spatial spectrum estimation of some of the basic algorithm- MusicEspritMp)

a program to simulate the position of the sun in the sky by two angles the altitude and the azimuth

说明：  多用户盲检测 kalman lms rls(Blind multi-user detection kalman lms rls)

四旋翼飞行器入门材料，由浅入深，适合初学者(Four-rotor entry materials, Deep, suitable for beginners)

fastmap 算法MATLAB实现，检索速度超快(fastmap MATLAB algorithms achieve super-fast retri )

Start with the runlocalization track.m which is the entrance function to your lab. This function reads two les determined by simoutle and maple input arguments which contain information about sensor readings and the map of the environment respectively, runs a loop for all the sensor readings and calls the ekf localize.m to perform one iteration of EKF localization on the readings and plots the estimation(red)/ground truth(green) and odometry(blue) information.(This lab consists of two parts: 1. A preparatory case study with a standard Kalman lter where you learn more about the behavior of the Kalman lter. Very little extra code is needed. 2. The main lab 1 problem in which you need to complete an implementation of an Extended Kalman lter based robot localization.)