cylinder2P

MATLAB实现基于蒙特靠罗方法的故障树仿真(MATLAB-based implementation methodology Montenegro Romania on Fault Tree Simulation)

说明：  这是一段利用matlab仿真抛骰子问题的蒙特卡洛算法，要求两个骰子的点数之和大于6并且第一个点数大于第二个点数(This is a simulation using matlab problem throwing dice Monte Carlo algorithm, to ask the two points of the dice and more than 6 points higher than the first and second points)

机组组合问题的粒子群算例，不错的算例，大家一起学习(Unit Commitment particle swarm example, a good example, we will study together)

超声声场的线性描述与仿真工具，可以作为超声系统的研究有用工具，根据不同的输入 ，得到不同的超声图像，验证自己的算法。(The linear ultrasonic sound field description and simulation tool that can serve as a useful tool for study of ultrasound systems, depending on the input, get a different ultrasound images to verify their algorithms.)

matlab 独立分量分析 fastica,icaplot,remmean,whiten，盲源分离,去均值,白化处理(matlab independent component analysis fastica, icaplot, remmean, whiten, blind source separation to the mean, whitening treatment)

GUI库飞信，精选ios编程学习源码，很好的参考资料。(GUI Fetion, selection of learning IOS programming source code, a good reference.)

在matlab里进行跌倒检测的模拟实验，定义三种特征，准确率高。(Test the fall in the matlab simulation experiment, define the three characteristics of high accuracy. )

一个求解李雅普诺夫指数的matlab程序,程序已经测试通过，适合matlab的初学者(Solving a Lyapunov exponent of the matlab program, the program has been tested for matlab beginner)

Some algorithms in the realm of ultrasound

This file compares three different versions of the Kalman filter. The Kalman filter is used for recursive parameter estimation. The Kalman filter can handle noisy measurements. The first implemented filter (fcn_KF) is the Kalman filter with standard update of the covariance matrix P. The covariance matrix reflects the uncertainties of the predictions. To improve the numerical stability Potter developed a square root update (fcn_KF_SRP) of the covariance matrix P. Another version is the square root covariance update via triangularization (fcn_KF_SRT). This file generates a model. Then the three Kalman filters perform an estimation of the model parameter. At the end the results are compared. Sources: Simon, D. (2006): Optimal state estimation Kaminski, P. (1971): Discrete Square Root Filtering: A Survey of Current Techniques Golub, G. (1996): Matrix Computations