jh17

filter kalman with matlab

mption estimation NTSS

包括matlab程序,c,vc++原代码和一些全景图拼接论文资料(This is the open source Hugin panorama stitching software source code from svn down under)

利用matlab求最大李雅普诺夫指数的wolf算法(Use matlab for the largest Lyapunov exponent wolf algorithm)

用MATLAB软件编程实现一般信道容量迭代算法。(Programming with MATLAB software, the channel capacity of a general iterative algorithm.)

在这项工作中所使用的方法是基于一个由大卫阿诺德教程。 http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf 这将运行该Logistic.m带来了图形用户界面。 1。放弃在列的格式文本文件中的x值 2。放弃在山口格式文本文件中的y值 3。的阴谋初始 按钮将绘制的分布 4。 查找适合 按钮，会找到最适合 5。 重置 将删除的情节（虽然我想打扫所有的领域 -没有时间） 5。地下K，糖尿病在下列公式中的值 Ÿ 等于k/（1+进出口（- G *的（十型录））） 6。上证所给出了squred误差之间的拟合函数与实际数据的总和 7。消委会提供的相关合作关系的实际数据拟合功能和效率(The method used in this work is based on a tutorial by David Arnold. http://online.redwoods.cc.ca.us/instruct/darnold/diffeq/logistic/logistic.pdf RUN The Logistic.m this will bring up the GUI. 1. Give the x values on a text file in column format 2. Give the y values on a text file in col format 3. Plot Initial Button will plot the distribution 4. Find Fit button will find the best fit 5. Reset will remove the plot (Although I wanted to clean all the fields- did not have time) 5. K, G, Dm are the values in the following equation y = K./(1+exp(-G*(x-Dm))) 6. SSE gives the sum of squred error between the fitted function and the actual data 7. CC give the correlation co-efficient between the fitted function and actual data)

多尺度特征熵，用于机械振动信号特征提取和故障诊断等方面(Multi-scale feature entropy for mechanical vibration signal feature extraction and fault diagnosis)

说明：  looking the nearest distance

将PID 控制与模糊控制的简便性、灵活性以及鲁棒性融为一体,构造了一个参数自整定模糊PID 控制器. 通过模糊控制规则在线调整PID 控制器的参数,并利用MATLAB 语言结合具体实例方便而快速地实现了该控制器的计算机仿真. 仿真结果表明:该控制方法提高了控制系统的动、静态特性,使系统获得了较好的性能.(The PID control and fuzzy control of the simplicity, flexibility and robustness of the integration, construction of a parameter self-tuning fuzzy PID controller. Through the fuzzy control rules online adjust the parameters of PID controller, and use specific examples of MATLAB language and quickly realize the convenience of the controller)

先定义允许状态S={(x,y)|x=0或3,y=0,1,2,3 x=y=1,2}, 允许决策D={(u,v)|u+v=1,2},求dk<-D(k=0,1,2,…,n),使 状态Sk<-S按照状态转移律S(k+1)=S(k)+(-1)^k*dk,由初始状态 S1=[3,3]经有限步到达状态S(n+1)=[0,0].此为求解多步决策问题( First define to allow the state S = ((x, y) | x = 0 or 3, y = 0,1,2,3 x = y = 1,2), to allow the decision-making D = ((u, v) | u+ v = 1,2), seek dk < -D (k = 0,1,2, ..., n), so that state Sk < -S state transition in accordance with law S (k+1) = S (k )+ (-1) ^ k* dk, from the initial state S1 = [3,3] by the finite-step to reach the status S (n+1) = [0,0]. This is a multi-step decision-making problem solving)