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Description: 1、该工具箱包括了混沌时间序列分析与预测的常用方法,有:
(1)产生混沌时间序列(chaotic time series)
Logistic映射 - \ChaosAttractors\Main_Logistic.m
Henon映射 - \ChaosAttractors\Main_Henon.m
Lorenz吸引子 - \ChaosAttractors\Main_Lorenz.m
Duffing吸引子 - \ChaosAttractors\Main_Duffing.m
Duffing2吸引子 - \ChaosAttractors\Main_Duffing2.m
Rossler吸引子 - \ChaosAttractors\Main_Rossler.m
Chens吸引子 - \ChaosAttractors\Main_Chens.m
Ikeda吸引子 - \ChaosAttractors\Main_Ikeda.m
MackeyGLass序列 - \ChaosAttractors\Main_MackeyGLass.m
Quadratic序列 - \ChaosAttractors\Main_Quadratic.m
(2)求时延(delay time)
自相关法 - \DelayTime_Others\Main_AutoCorrelation.m
平均位移法 - \DelayTime_Others\Main_AverageDisplacement.m
(去偏)复自相关法 - \DelayTime_Others\Main_ComplexAutoCorrelation.m
互信息法 - \DelayTime_MutualInformation\Main_Mutual_Information.m
(3)求嵌入维(embedding dimension)
假近邻法 - \EmbeddingDimension_FNN\Main_FNN.m
Cao方法 - \EmbeddingDimension_Cao\Main_EmbeddingDimension_Cao.m
(4)同时求时延与嵌入窗(delay time & embedding window)
CC方法 - \C-C Method\Main_CC_Luzhenbo.m
(5)求关联维(correlation dimension)
GP算法 - \CorrelationDimension_GP\Main_CorrelationDimension_GP.m
(6)求K熵(Kolmogorov Entropy)
GP算法 - \KolmogorovEntropy_GP\Main_KolmogorovEntropy_GP.m
STB算法 - \KolmogorovEntropy_STB\Main_KolmogorovEntropy_STB.m
(7)求最大Lyapunov指数(largest Lyapunov exponent)
小数据量法 - \LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein1.m
\LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein2.m
\LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein3.m
\LargestLyapunov_Rosenstein\Main_LargestLyapunov_Rosenstein4.m
(8)求Lyapunov指数谱(Lyapunov exponent spectrum)
BBA算法 -
\LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA1.m
\LyapunovSpectrum_BBA\Main_LyapunovSpectrum_BBA2.m
(9)求二进制图形的盒子维(box dimension)和广义维(genealized dimension)
覆盖法 -
\BoxDimension_2D\Main_BoxDimension_2D.m
\GeneralizedDimension_2D\Main_GeneralizedDimension_2D.m
(10)求时间序列的盒子维(box dimension)和广义维(genealized dimension)
覆盖法 -
\BoxDimension_TS\Main_BoxDimension_TS.m
\GeneralizedDimension_TS\Main_GeneralizedDimension_TS.m
(11)混沌时间序列预测(chaotic time series prediction)
RBF神经网络一步预测 - \Prediction_RBF\Main_RBF.m
RBF神经网络多步预测 - \Prediction_RBF\Main_RBF_MultiStepPred.m
Volterra级数一步预测 - \Prediction_Volterra\Main_Volterra.m
Volterra级数多步预测 - \Prediction_Volterra\Main_Volterra_MultiStepPred.m
(12)产生替代数据(Surrogate Data)
随机相位法 - \SurrogateData\Main_SurrogateData.m
2、在matlab环境中首先运行install.m,将工具箱所在路径添加至matlab
3、各子目录下以Main_开头的文件即是主程序文件,直接按快捷键F5运行即可
4、工具箱中所有程序均在Matlab6.5和Matlab7.1环境中调试通过，不能保证在Matlab其它版本正确运行。
5、工具箱中部分功能为试用版，敬请谅解！
6、
作者：陆振波，海军工程大学
欢迎同行来信交流与合作，更多文章与程序下载请访问我的个人主页
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Author: niuchao0511 |
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Description: 自己编写的matlab程序 实现数字图像的模板匹配和相关算法，以及亚像素算法 压缩包中含源程序，实例图片和模板，以及模板生成算法。 使用方法： 1、设定路径 2、打开threshold，F5 有问题欢迎提问。
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Size: 956084 |
Author: isharesin |
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Description: wavelet小波滤波原代码,非常经典的代码程序-wavelet wavelet filter the original code, the code is very classical procedures
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Size: 3072 |
Author: 黄心 |
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Description: 基于图象的数据隐写和提取matlab代码-image-based data extraction and steganalysis Matlab code
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Size: 9216 |
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Description: 自己现在在做的论文用到灰度共生矩阵，找了好久都没找到很好用的，所以自己就编了个，不很完美，但绝对可用，已经实现过。包括灰度共生矩阵的生成以及一些特征参数。f1，二阶距。f2，对比度。f3，相关。f5，逆差距。f6，和平均。f7，和方差。f9，差平均。f10，差方差。请不吝赐教。-Now do their own papers in the gray co-occurrence matrix used to find for a long time to find no good use, so it编了个own, not very ideal, but it is available, have been implemented. Including the generation of gray-level co-occurrence matrix as well as some characteristic parameters. f1, the second order from. f2, contrast. f3, related. f5, inverse gap. f6, and the average. f7, and the variance. f9, the average difference. f10, difference variance. Please wing.
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Size: 2048 |
Author: 周鹏 |
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Description: F5隐写算法的实现，F5算法，一种适用于JPG图像的隐写算法-F5 steganography algorithm, F5 algorithm, a JPG image for steganalysis algorithm
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Size: 494592 |
Author: Fbw |
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Description: This a three fredom degree robot modeled in simulink which is controled ( 3D position control) by a PID. you set the reference trajectory and the robot controled by the PID follows the trajectory in a good way. In order to run open parametros_luis.m run it ( press f5) and then run the simulink.-This is a three fredom degree robot modeled in simulink which is controled ( 3D position control) by a PID. you set the reference trajectory and the robot controled by the PID follows the trajectory in a good way. In order to run open parametros_luis.m run it ( press f5) and then run the simulink.
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Size: 1203200 |
Author: ricce |
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Description: 数字密写与密写技术 F5的Matlab源程序-Digital Steganography Steganography technology and F5 s Matlab source code
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Size: 1024 |
Author: 柳絮 |
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Description: matlab for for f5 seganography
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Size: 611328 |
Author: jack abraham |
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Description: Stegano in matlab F5 algorithm
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Size: 172032 |
Author: suchit |
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Description: F5 matlab algorithm
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Size: 9216 |
Author: suchit |
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Description:
聚类分析Matlab工具箱 Version1.0
Clustering Analysis Matlab Toolbox - Trial Version 1.0
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1、该工具箱包括了常用的三种K-Means聚类分析和ISODATA聚类分析:
...\KMeans\Main_KMeans.m 标准 K-Means 聚类 - 主函数
...\KMeans\Main_FuzzyKMeans.m 模糊 K-Means 聚类 - 主函数
...\KMeans\Main_FuzzyKMeansImproved.m 改进的模糊 K-Means 聚类 - 主函数
...\Isodata\Main_Isodata.m ISODATA聚类算法 - 主函数
2、在matlab环境中首先运行install.m,将工具箱所在路径添加至matlab
3、各子目录下以Main_开头的文件即是主程序文件,直接按快捷键F5运行即可
4、工具箱中所有程序均在 "Windows XP SP2 + Matlab7.1" 环境中调试通过，不能保证在其它平台上正确运行。
5、
作者：陆振波，海军工程大学
欢迎同行来信交流与合作，更多文章与程序下载请访问我的个人主页
电子邮件：luzhenbo@yahoo.com.cn
个人主页：http://blog.sina.com.cn/luzhenbo2-
聚类分析Matlab工具箱 Version1.0
Clustering Analysis Matlab Toolbox- Trial Version 1.0
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1、该工具箱包括了常用的三种K-Means聚类分析和ISODATA聚类分析:
...\KMeans\Main_KMeans.m 标准 K-Means 聚类- 主函数
...\KMeans\Main_FuzzyKMeans.m 模糊 K-Means 聚类- 主函数
...\KMeans\Main_FuzzyKMeansImproved.m 改进的模糊 K-Means 聚类- 主函数
...\Isodata\Main_Isodata.m ISODATA聚类算法- 主函数
2、在matlab环境中首先运行install.m,将工具箱所在路径添加至matlab
3、各子目录下以Main_开头的文件即是主程序文件,直接按快捷键F5运行即可
4、工具箱中所有程序均在 "Windows XP SP2+ Matlab7.1" 环境中调试通过，不能保证在其它平台上正确运行。
5、
作者：陆振波，海军工程大学
欢迎同行来信交流与合作，更多文章与程序下载请访问我的个人主页
电子邮件：luzhenbo@yahoo.com.cn
个人主页：http://blog.sina.com.cn/luzhenbo2
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Size: 39936 |
Author: 陆振波 |
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Description: 本文件可以用来精确计算M-K趋势值，以及倾斜度。 matlab的m文件 直接点击F5运行-This file can be used to accurately calculate the the MK trend values, as well as the inclination. matlab m file directly Click F5 to run
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Size: 3072 |
Author: 王跃峰 |
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Description: monopulse radar is simulated by matlab sourch code please press f5
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Size: 6144 |
Author: feel |
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Description: 利用matlab的GUI设计俄罗斯方块游戏
按键说明：
W、向上箭头、小键盘5：变换形状；
A、向左箭头、小键盘1：左移；
S、向下箭头、小键盘2：快速下移；
D、向右箭头、小键盘3：右移；
空格键：暂停；
F5或CTRL+R：开始；
CTRL+S：停止；
快捷键可以参考菜单。-GUI design using matlab Tetris game Key Description: W, up arrow, keypad 5: Transform shape A, left arrow, keypad 1: left S, down arrow, keypad 2: Fast Down D, right arrow keypad 3: Right Spacebar: Pause F5 or CTRL+R: start CTRL+S: Stop shortcut keys can refer to the menu.
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Size: 3264512 |
Author: micheal |
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Description: 隐写方法nsf5 是f5的改进型 用matlab编写-steganalysis nsf5
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Size: 259072 |
Author: 季文驰 |
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Description: nsf5隐写算法，DCT的升级版，MATLAB实现，可以抵抗一般的DCT隐写分析(The algorithm nsF5 evolved from the F5 algorithm originally proposed by Andreas Westfeld in 2001. F5 decreases the absolute value of DCT coefficients and incorporates matrix embedding - a coding scheme that decreases the number of changes and consequently increases the steganographic security. F5 embeds only into nonzero AC DCT coefficients; if a coefficient becomes zero after embedding (the so called shrinkage), the same bit is reembedded at the next coefficient. Shrinkage substantially decreases the embedding efficiency as the values +1 and -1 are the most frequent nonzero DCT coefficients in natural images.)
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Size: 100352 |
Author: 人气呆毛选手
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Description: 图像隐藏F5算法源代码，使用matlab实现。经典的图像隐写算法之一，matlab代码，测试可用。(Image hiding F5 algorithm source code, using MATLAB to achieve. One of the classic image steganography algorithms, matlab code, the test is available.)
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Size: 793600 |
Author: saasdsa |
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Description: 这是哈尔滨工业大学数字信号处理课的课程设计作业，利用窗函数涉及滤波器分别对自己设定的测试信号和老师给定的连续频谱信号进行滤波。
我编写时先后在同一个M文件中写了很多的画图语句，如果直接F5运行，它们会互相覆盖，使用时请将需要的绘图代码复制到命令行中重新运行。(This is the course design assignment of Digital Signal Processing Course of Harbin University of Technology. The window function is used to filter the test signal set by yourself and the continuous spectrum signal given by the teacher.)
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Size: 3072 |
Author: Wicjpy |
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