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Description: 神经网络训练根据Kolmogorov定理，输入层有14个节点，所以中间层有29个节点
％中间层神经元的传递函数为 tansig
％输出层有8个节点，其神经元传递函数为logsig
％训练函数采用traingdx-neural network training under the Kolmogorov theorem, input layer has 14 nodes, Therefore, the intermediate layer has 29% of nodes middle layer neurons in the transfer function for the output layer tansig% have eight nodes, its neuron transfer function for the training function logsig% used traingdx
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Author: 陈胜 |
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Description:
This software implements stereo algorithms described in the following papers:
Vladimir Kolmogorov and Ramin Zabih
\"Multi-Camera scene Reconstruction via Graph Cuts\"
In: European Conference on Computer Vision, May 2002.
Vladimir Kolmogorov and Ramin Zabih
\"Computing Visual Correspondence with Occlusions using Graph Cuts\"
In: International Conference on Computer Vision, July 2001.
and
Yuri Boykov, Olga Veksler and Ramin Zabih
\"Markov Random Fields with Efficient Approximations\"
In: IEEE Computer Vision and Pattern Recognition Conference, June 1998. -stereo algorith ms described in the following papers : Vladimir Kolmogorov and Ramin Zabih "Multi-Ca mera scene Reconstruction via Graph Cuts "In : European Conference on Computer Vision, May 2002. Vladimir Kolmogorov and Ramin Zabih " Correspondence with Visual Computing Occlusi ons using Graph Cuts "In : International Conference on Computer Vision, July 2001. and Yuri Boykov, Olga Veksler and Ramin Zabih "Markov Random Fie Efficient Bayesian lds with "In : IEEE Computer Vision and Pattern Recognition C onference, democratic governance.
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Size: 915541 |
Author: Chen |
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Description: 关于混沌Kolmogorov熵的计算程序，在前人基础上自己编写的。已经用过很多混沌时间序列K熵的计算。
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Size: 3429 |
Author: 张真 |
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Description: The two algorithms are max-product belief propagation (BP, Pearl 88) and
sequential tree-reweighted max-product message passing (TRW-S, Kolmogorov 05)
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Size: 40600 |
Author: wdbigboy |
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Description: Kolmogorov 熵的计算 - GP算法
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Size: 9845 |
Author: 陆振波 |
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Description: Kolmogorov 熵的计算 - STB算法
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Size: 5837 |
Author: 陆振波 |
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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: Kolmogorov将graph cut应用于立体视觉的实例源码match-v3.1
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Size: 1259520 |
Author: lcg2618@163.com |
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Description: computing dense correspondence #
# (disparity map) between two images using graph cuts
This software implements stereo algorithms described in the following papers:
Vladimir Kolmogorov and Ramin Zabih
"Multi-Camera scene Reconstruction via Graph Cuts"
In: European Conference on Computer Vision, May 2002.
Vladimir Kolmogorov and Ramin Zabih
"Computing Visual Correspondence with Occlusions using Graph Cuts"
In: International Conference on Computer Vision, July 2001.
and
Yuri Boykov, Olga Veksler and Ramin Zabih
"Markov Random Fields with Efficient Approximations"
In: IEEE Computer Vision and Pattern Recognition Conference, June 1998.
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Size: 1319269 |
Author: newship@126.com |
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Description:
This software implements stereo algorithms described in the following papers:
Vladimir Kolmogorov and Ramin Zabih
"Multi-Camera scene Reconstruction via Graph Cuts"
In: European Conference on Computer Vision, May 2002.
Vladimir Kolmogorov and Ramin Zabih
"Computing Visual Correspondence with Occlusions using Graph Cuts"
In: International Conference on Computer Vision, July 2001.
and
Yuri Boykov, Olga Veksler and Ramin Zabih
"Markov Random Fields with Efficient Approximations"
In: IEEE Computer Vision and Pattern Recognition Conference, June 1998. -stereo algorith ms described in the following papers : Vladimir Kolmogorov and Ramin Zabih "Multi-Ca mera scene Reconstruction via Graph Cuts "In : European Conference on Computer Vision, May 2002. Vladimir Kolmogorov and Ramin Zabih " Correspondence with Visual Computing Occlusi ons using Graph Cuts "In : International Conference on Computer Vision, July 2001. and Yuri Boykov, Olga Veksler and Ramin Zabih "Markov Random Fie Efficient Bayesian lds with "In : IEEE Computer Vision and Pattern Recognition C onference, democratic governance.
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Size: 1393664 |
Author: Chen |
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Description: 混沌理论kolmogirov熵参数研究用于通信流量中熵研究-Chaos Theory kolmogirov parameters of entropy in the entropy flow for communications research
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Size: 33792 |
Author: 张延中 |
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Description: 在Matlab环境下，关于近似熵的计算程序
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Size: 1024 |
Author: Nick |
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Description: 本代码执行Edmonds algorithm来进行最小代价函数，实现图形最佳匹配。参考文献："Blossom V: A new implementation of a minimum cost perfect matching algorithm."Vladimir Kolmogorov.
-Edmonds algorithm the implementation of the code for the smallest cost function to achieve the best matching graphics. References:
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Size: 56320 |
Author: Zhongren Wang |
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Description: 混沌计算程序源文件——包括计算Lyapunov指数的5种方法：C-C算法,最小数据量算法，G-P算法，关联维数法，互信息量法。-the original programs to calculate chaos in order to count the Lyapunov index out. Including 5 such methods: c-c method, minimum data method, G-P, mutual information method and correlation dimentional method.
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Author: 碶衫 |
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Description: Lyapunov,关联维和Kolmogorov熵在混沌动力系统中的应用chen
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Author: renzhangyan |
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Description: this the Grqphcut optimization by kolmogorov-this is the Grqphcut optimization by kolmogorov
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Author: sthupakula |
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Description: 实现了An Experimental Comparison of
Min-Cut/Max-Flow Algorithms for
Energy Minimization in Vision
[Yuri Boykov and Vladimir Kolmogorov]中的新的最大流算法，经过实验验证比传统的最大流算法效率更高-Achieved An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision [Yuri Boykov and Vladimir Kolmogorov] in the new maximum flow algorithm, after experimental verification than the traditional maximum flow algorithm more efficient
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Size: 1024 |
Author: ray |
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Description: A set (e.g. an image) is called "fractal" if it displays self-similarity: it can be split into parts, each of which is (at least approximately) a reduced-size copy of the whole.
A possible characterisation of a fractal set is provided by the "box-counting" method: The number N of boxes of size R needed to cover a fractal set follows a power-law, N = N0 * R^(-DF), with DF<=D (D is the dimension of the space, usually D=1, 2, 3).
DF is known as the Minkowski-Bouligand dimension, or Kolmogorov capacity, or Kolmogorov dimension, or simply box-counting dimension.
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Author: piri_small |
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Description: 用于计算混沌分析中的柯尔莫哥洛夫熵的STB算法-for compute the Kolmogorov entropy
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Size: 1024 |
Author: 张鲲鹏 |
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Description: 柯尔莫哥洛夫熵（Kolmogorov熵，以下简称K熵）是刻划混沌系统的一个重要的量。在不同类型的动力学系统中，K熵的数值是不同的。本程序可以计算kolmogorov熵(Kolmogorov entropy (hereinafter referred to as K entropy) is an important quantity for describing chaotic systems. In different types of dynamical systems, the K entropy is different. This procedure can calculate Kolmogorov entropy.)
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Author: xiaojinyongjoy |
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